test.bat.commands

#===========================================================
#
# description
#
#===========================================================

# input description
java yi.iu.imgdb.desc.gui.ImageDescApplication

# list images have several keywords (can be all the keywords, or any of the keywords)
java yi.iu.imgdb.desc.ImgDescManager$ShowImages -d set_name.desc -a -p -k object > set_name.object.html
java yi.iu.imgdb.desc.ImgDescManager$ShowImages -d sd.mmtest.desc -a -p -k tree > sd.mmtest.tree.html
java yi.iu.imgdb.desc.ImgDescManager$ShowImages -d gt.desc -a -p -k bridge
java yi.iu.imgdb.desc.ImgDescManager$ShowImages -d s9.bg.desc -k ground -o t.desc -p > t.html

# list images (one for each keyword)
java yi.iu.imgdb.desc.ImgDescManager$ShowImages2 -d desc_name.desc

# desc statistics
java yi.iu.imgdb.desc.ImgDescManager$FilterKeywords -d set_name.desc -t 0 > set_name.stat
java yi.iu.imgdb.desc.ImgDescManager$FilterKeywords -d gt.concept.desc -t 0 > gt.concept.stat
java yi.iu.imgdb.desc.ImgDescManager$FilterKeywords -d sim.dir.desc -t 0 > sim.dir.stat
java yi.iu.imgdb.desc.ImgDescManager$FilterKeywords -d sim.dir.count.desc -t 0 > sim.dir.count.stat

# filter keywords
java yi.iu.imgdb.desc.ImgDescManager$FilterKeywords -d old_desc.desc -o new_desc.desc -t threshold
java yi.iu.imgdb.desc.ImgDescManager$FilterKeywords -d mt.desc -o mt.50.desc -t 50
java yi.iu.imgdb.desc.ImgDescManager$FilterKeywords -d mt.desc -o mt.30-50.desc -t 30

# get only the image name
java yi.iu.imgdb.desc.ImgDescManager$FilterKeywords -d t.polar_bear.desc -o t.rst -t 800

# clean the keywords
java yi.iu.imgdb.desc.ImgDescManager$FilterKeywords -d s8.bg.desc -o t.rst


#===========================================================
#
# color seg
#
#===========================================================
java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f cs
java yi.iu.imgdb.learn.FeatureUtil$Generate -i groundtruth/football/football05.jpg -f cs,CIELabLabeledRgnFts,256,256
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  set_name.list -f cs -h set_name
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f cs -h set_name
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d set_name.desc -r cs


#===========================================================
#
# texture seg
#
#===========================================================
java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f ts
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  set_name.list -f ts -h set_name
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f ts -h set_name
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d set_name.desc -r ts

#===========================================================
#
# structure
#
#===========================================================
java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f st
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  set_name.list -f st -h set_name
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  t.list -f st
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d gt.desc -r st

# learn on selected attributes
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d data_center.idc -a yi.iu.imgdb.learn.GMMNNAdapter -f LineStructGenerator:0:1:2:3:4:5:6:9:10:11 -n 5

#===========================================================
#
# color patch
#
#===========================================================
java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f cp
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  set_name.list -f cp -h set_name
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f cp -h set_name
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d set_name.desc -r cp

#===========================================================
#
# texture patch
#
#===========================================================
java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f tp
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  set_name.list -f tp -h set_name
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f tp -h set_name
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d set_name.desc -r tp

#===========================================================
#
# Prominent Color
#
#===========================================================
java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f pc
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  set_name.list -f pc -h set_name
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f pc -h set_name
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d set_name.desc -r pc

#===========================================================
#
# DominantColor Color
#
#===========================================================
java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f yi.iu.processor.color.DPU_DominantColor,CIELabLabeledRgnFts,256,128
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  crystal.list -f yi.iu.processor.color.DPU_DominantColor,CIELabLabeledRgnFts,256,128 -h crystal

#===========================================================
#
# Mean Shift
#
#===========================================================
#resample the image
perl ..\tools\bp.pl yi.iu.processor.resample.Resampler set_name.list -f image_path/ -t feature_path/ -s 128
perl ..\tools\bp.pl yi.iu.processor.resample.Resampler crystal.list -f h:/images/ -t rst/ft/ -s 128
#edison script generation
java yi.iu.processor.color.seg.mb.MeanShiftBasedImageSegmenter$GenerateScript -i set_name.list -s 16 -r 6.5 -m 64 -p MEDIUM  > set_name.edison.eds
java yi.iu.processor.color.seg.mb.MeanShiftBasedImageSegmenter$GenerateScript -i crystal.list -s 16 -r 6.5 -m 64 -p MEDIUM  > crystal.edison.eds
# edison mean shift based image segmentation
edison set_name.edison.eds
edison crystal.edison.eds
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate set_name.list -f ms -h set_name
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate crystal.list -f ms -h crystal
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f ms -h set_name
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d set_name.desc -r ms
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d set_name.MeanShiftBasedImageSegmenter.FeatureRepresentationList.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5


#===========================================================
#
# blobworld
#
#===========================================================
# rescale the image
perl ..\tools\bp.pl yi.iu.processor.resample.Resampler$Scale4Blobworld crystal.list -f h:/images/ -t rst/ft/
perl ..\tools\bp.pl yi.iu.processor.resample.Resampler$Scale4Blobworld t.list -f h:/images/ -t rst/ft/
# run blobworld in matlab
change parameter FILELIST in params.m (blobworld\params.m)
run script in matlab
# generate representable feature
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate crystal.list -f bw -h crystal
# represent
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent nima.list -f BlobworldSegmenter -h nima
# image data center
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d nima.desc -r bw
# learn
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d nima.BlobworldSegmenter.idc -a yi.iu.imgdb.learn.GMMNNAdapterMean -f BlobworldSegmenter -o tree -l
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d nima.ms+bw.idc -a yi.iu.imgdb.learn.GMMNNAdapterMean -f ms+bw:1:2:3:7:8 -o tree -l


#===========================================================
#
# feature related
#
#===========================================================
# see feature vectors
java yi.iu.processor.color.reg/CIELabLabeledRgnFts$DemoFeatureColors -i rst/ft/groundtruth/arborgreens/arborgreens01.jpg.s256.r128.KMeansHSISeg.CIELabLabeledRgnFts.ser


#===========================================================
#
# represent (in different format)
#
#===========================================================
# polygon representation
java yi.iu.imgdb.learn.FeatureUtil$Represent -i image_name -f cs -r cs,PolygonFeatureRepresentationList -h set_name
# ellipse representation
java yi.iu.imgdb.learn.FeatureUtil$Represent -i image_name -f cs -r cs,EllipseFeatureRepresentationList -h set_name

#===========================================================
#
# intersection
#
#===========================================================
# first represent the feature with label
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f ms -r ms,LabeledRgnFtRepList -h set_name
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f bw -r bw,LabeledRgnFtRepList -h set_name
# intersect meanshift and blobworld
perl ..\tools\bp.pl yi.iu.imgdb.learn.LabeledRgnFtRepList$IntersectRegionRepresentations set_name.list -r ms,LabeledRgnFtRepList;bw,LabeledRgnFtRepList;ms+bw,LabeledRgnFtRepList,128,0,0 -h set_name -t size_threshold
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d set_name.desc -r ms+bw,LabeledRgnFtRepList,128,0,0

#===========================================================
#
# image data center related
#
#===========================================================

# generate
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d set_name.desc -r representation_desc
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d t.desc -r pc
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d t.desc -r ms

# merge
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter$MergeImgDataCenters -d data_center_1.idc -m data_center_2.idc -o data_center_merged.idc
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter$MergeImgDataCenters -d t.DPU_GongProminentColor.FeatureRepresentationList.idc -m t.MeanShiftBasedImageSegmenter.FeatureRepresentationList.idc -o t.pc+ms.idc

# replace description (totally replace)
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter$ReplaceDescriptions -i data_center.idc -d new_desc.desc -o new_data_center_name.ide -r
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter$ReplaceDescriptions -i mt.50.lab+eng.idc -d mt.30-50.desc -o mt.30-50.lab+eng.idc -r
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter$ReplaceDescriptions -i gt.ts+cs+st.idc -d gt.concept.desc -o gt.concept.ts+cs+st.idc -r

# replace description (replace only the specified ones)
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter$ReplaceDescriptions -i data_center.idc -d new_desc.desc -o new_data_center_name.idc

# print
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter$Print -d data_center.idc
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter$Print -d t.pc+ms.idc

# generate the html file of all the images (work for only some of the features)
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter$GenHtml -d t.pc+ms.idc > t.pc+ms.html



#===========================================================
#
# experiments
#
#===========================================================

# 5-folds
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d data_center.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 [-o obj] [-r rst_output_file] [-e output_experiment]
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.ts+cs+st.idc -a yi.iu.imgdb.learn.GMMNNAdapter -f LineStructGenerator:0:1:2:3:4:5:6:9:10:11 -n 5
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMAdapter -n 5
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.FeatureRepresentationList.KMeansHSISeg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.c.t1.mm.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.c.t1.mm.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o mountains -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.c.t1.mm.idc -a yi.iu.imgdb.learn.GMMNNAllObjAdapter -n 5
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.c.t1.mm.idc -a yi.iu.imgdb.learn.GMMNNAdapterNoCompMean -n 5
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.ct1.mm.idc -a yi.iu.imgdb.learn.GMMNNAllObjAdapter -n 5
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.mmtest.KMeansHSISeg.FeatureRepresentationList.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o stadium -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.mmtest.KMeansHSISeg.FeatureRepresentationList.idc -a yi.iu.imgdb.learn.GMMNNAllObjAdapter -o stadium -n 5
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.ts+cs+st.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o stone -n 5

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d mt.50.idc -a yi.iu.imgdb.learn.GMMNNAdapter
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d mt.50.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o 1,2,3,4,6,7,11,12,13,14,15,16 -f mt:15:16:17:24:25:26:27:28:29:30:31:32:33:34:35
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d mt.50.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o 1,2,3,4,6,7,11,12,13,14,15,16 -f mt:0:2:15:16:17:24:27:30:33
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d mt.50.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o 1,2,3,4,6,7
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d mt.50.lab+eng.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o 1,2,3,4,6,7,11,12,13,14,15,16
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d mt.50.idc -a yi.iu.imgdb.learn.GMMNNAdapter > out
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d mt.50.lab+eng.idc -a yi.iu.imgdb.learn.GMMNNAdapter > out.1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d mt.30-50.lab+eng.idc -a yi.iu.imgdb.learn.GMMNNAdapter > out.1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.concept.ts+cs+st.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 -o indoor,italy,geneva,australia,swiss_moutains,indonesia,japan > out
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.concept.ts+cs+st.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 -o cannon_beach,green_lake,sanjuans,yellowstone,spring_flowers,iran,park,asian > out
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.concept.ts+cs+st.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 -o barcelona,european,greenland > out
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.bg.KMeansHSISeg.FeatureRepresentationList.idc -a yi.iu.imgdb.learn.em.GMMAdapter -e > out
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.bg.ColorPatchSeg.FeatureRepresentationList.idc -a yi.iu.imgdb.learn.em.GMMAdapter -e > out
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.bg.ModelBasedMRFSegmenter.FeatureRepresentationList.idc -a yi.iu.imgdb.learn.em.GMMAdapter -e > out

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.pc+tp.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 > out
perl ..\tools\bp2.pl yi.iu.imgdb.learn.StatModelFoldExperiment -o obj.list -d wang.cp+tp.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5
perl ..\tools\bp2.pl yi.iu.imgdb.learn.StatModelFoldExperiment -o obj.list -d wang.cp+pc+tp.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.FeatureRepresentationList.ColorPatchSeg.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 > out
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.FeatureRepresentationList.DPU_GongProminentColor.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 > out

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.cs+ts+stm4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o beach,mountain -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.cs+ts+stm4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o cherry_tree -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.FeatureRepresentationList.KMeansHSISeg.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o cherry_tree -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.FeatureRepresentationList.KMeansHSISeg.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o football_field -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.FeatureRepresentationList.KMeansHSISeg.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o flower -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.FeatureRepresentationList.KMeansHSISeg.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o beach -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.cs+ts+stm4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o cherry_tree -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.cs+ts+stm4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o bridge -e

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.bg.KMeansHSISeg.FeatureRepresentationList.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 > out 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.EMVNNAdapter -n 5 > out 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.EMVNNAdapter -n 5 > out 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.c.t1.mm.idc -a yi.iu.imgdb.learn.EMVNNAdapter -n 5 > out 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.c.t1.mm.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 > out 2>&1

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.ct1.bg.ser -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 > out 2>&1
/* results for ICPR04 paper */
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.ct1.bg.ser -a yi.iu.imgdb.learn.em.GMMAdapter -n 5 > out 2>&1

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.ts+st4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 > out 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.cs+st4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 > out 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.cs+ts.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 > out 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.cs+ts+st4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -n 5 > out 2>&1

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=8 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m8.i5.o4.gg > out.8 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=10 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m10.i5.o4.gg > out.10 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=6 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m6.i5.o4.gg > out.6 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=12 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m12.i5.o4.gg > out.12 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=14 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m14.i5.o4.gg > out.14 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=16 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m16.i5.o4.gg > out.16 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=2 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m2.i5.o4.gg > out.2 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=3 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m3.i5.o4.gg > out.3 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=4 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m4.i5.o4.gg > out.4 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=5 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m5.i5.o4.gg > out.5 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=7 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m7.i5.o4.gg > out.7 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=9 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m9.i5.o4.gg > out.9 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=11 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m11.i5.o4.gg > out.11 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=13 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m13.i5.o4.gg > out.13 2>&1
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -Dyi.iu.imgdb.learn.em.GMMMMAdapter.NUM_COMP_IN_GMM_COMP=15 -d sd.ct1.mm.bg.idc -a yi.iu.imgdb.learn.em.GMMMMAdapter -n 5 -r sd.ct1.mm.bg.GMMMMAdapter.rst.m15.i5.o4.gg > out.15 2>&1



# learn on selected attributes
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d data_center.idc -a yi.iu.imgdb.learn.GMMNNAdapter -f LineStructGenerator:6:9:10:11,KMeansHSISeg -n 5
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d gt.cs+ts+stm4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -f LineStructGenerator:6:9:10:11,KMeansHSISeg,ColorGuidedTextureSegNCC -n 5

# output learned model
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d data_center.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o object -l

# retest
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$Test -e experiment_file [-c 1] [-l|-t] [-i img_list_file] -r rst_file_name.rst
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$Test -e sd.mmtest.KMeansHSISeg.FeatureRepresentationList.GMMNNAdapter.stadium.exp -t -r t.rst
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$Test -e gt.cs+ts+stm4.GMMNNAdapter.beach,mountain.exp -i t.list

# locate object
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e experiment.exp [-c 0] [-o object] [-l|t|-i img_list]
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e VACE1.KMeansHSISeg.FeatureRepresentationList.GMMNNAdapter.exp -o runway -t
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e sd.test.bg.KMeansHSISeg.FeatureRepresentationList.GMMAdapter.exp -i t.list
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e sd.test.bg.ColorPatchSeg.FeatureRepresentationList.GMMAdapter.exp -f cp,CIELabLabeledRgnFts,128,8 -i t.list
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e gt.cs+ts+stm4.GMMNNAdapter.beach,mountain.exp -i t.list
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e gt.cs+ts+stm4.GMMNNAdapter.cherry_tree.exp -i t.list
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e gt.FeatureRepresentationList.KMeansHSISeg.GMMNNAdapter.cherry_tree.exp -i t.list > gt.FeatureRepresentationList.KMeansHSISeg.GMMNNAdapter.cherry_tree.loc.html 
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e gt.FeatureRepresentationList.KMeansHSISeg.GMMNNAdapter.football_field.exp -i t.list
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e gt.FeatureRepresentationList.KMeansHSISeg.GMMNNAdapter.flower.exp -i t.list > gt.FeatureRepresentationList.KMeansHSISeg.GMMNNAdapter.flower.loc.html
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e gt.cs+ts+stm4.GMMNNAdapter.cherry_tree.exp -i t.list > gt.cs+ts+stm4.GMMNNAdapter.cherry_tree.loc.html
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e gt.cs+ts+stm4.GMMNNAdapter.bridge.exp -i t.list > gt.cs+ts+stm4.GMMNNAdapter.bridge.loc.html

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.KMeansHSISeg.FeatureRepresentationList.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o cherry_tree -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e sd.test.KMeansHSISeg.FeatureRepresentationList.GMMNNAdapter.cherry_tree.exp -y -o cherry_tree > sd.test.KMeansHSISeg.FeatureRepresentationList.GMMNNAdapter.cherry_tree.loc.html

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.cs+ts.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o cheetah -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e sd.test.cs+ts.GMMNNAdapter.cheetah.exp -y -o cheetah > sd.test.cs+ts.GMMNNAdapter.cheetah.loc.html

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.BlobworldSegmenter.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o cheetah -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e sd.test.BlobworldSegmenter.GMMNNAdapter.cheetah.exp -y -o cheetah > sd.test.BlobworldSegmenter.GMMNNAdapter.cheetah.loc.html

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.MeanShiftBasedImageSegmenter.FeatureRepresentationList.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o cheetah -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e sd.test.MeanShiftBasedImageSegmenter.FeatureRepresentationList.GMMNNAdapter.cheetah.exp -y -o cheetah > sd.test.MeanShiftBasedImageSegmenter.FeatureRepresentationList.GMMNNAdapter.cheetah.loc.html

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d sd.test.bw+ms.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o cheetah -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e sd.test.bw+ms.GMMNNAdapter.cheetah.exp -y -o cheetah > sd.test.bw+ms.GMMNNAdapter.cheetah.loc.html

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.cs+st4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o buses -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e wang.cs+st4.GMMNNAdapter.buses.exp -y -o buses > wang.cs+st4.GMMNNAdapter.buses.loc.html

java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d wang.cs+st4.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o buildings -e
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment$LocateObject -e wang.cs+st4.GMMNNAdapter.buildings.exp -y -o buildings > wang.cs+st4.GMMNNAdapter.buildings.loc.html


#===========================================================
#
# about the results
#
#===========================================================

# see the roc chart and roc scores
java yi.iu.imgdb.learn.StatTestRst -d data_center.idc -r written_result.rst
java yi.iu.imgdb.learn.StatTestRst -d sd.ct1.mm.bg.idc -r sd.test.c.t1.mm.GMMNNAdapter.rst

# new roc analysis (use desc instead of idc)
java yi.iu.imgdb.learn.StatTestRst$ROCAnalysis -d img_set.desc -r test_rst.rst
java yi.iu.imgdb.learn.StatTestRst$ROCAnalysis -d freefoto.bhs.desc -r freefoto.bhs.st+cs.GMMNNAdapter.rst

# see the image sorted by their scores
java yi.iu.imgdb.learn.StatTestRst -d data_center.idc -r written_result.rst -h
java yi.iu.imgdb.learn.StatTestRst -d VACE1.cs+ts+st.idc -r VACE1.cs+ts+st.GMMNNAdapter.rst -h
java yi.iu.imgdb.learn.StatTestRst -d gt.51.ts+cs+st.idc -r gt.51.ts+cs+st.GMMNNAdapter.rst -h
java yi.iu.imgdb.learn.StatTestRst$ROCAnalysis -d freefoto.bhs.desc -r freefoto.bhs.st+cs.GMMNNAdapter.rst -h
java yi.iu.imgdb.learn.StatTestRst$ROCAnalysis -d sd.test.desc.withpath -r sd.ct1.bg.sg.cie.rst.ser -h

# generate web demo
java yi.iu.imgdb.learn.StatTestRst$GenWebDemo -d gt.51.ts+cs+st.idc -r gt.51.ts+cs+st.GMMNNAdapter.rst

# classify analysis
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d data_center.idc -r written_result.rst
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.FeatureRepresentationList.KMeansHSISeg.idc -r wang.FeatureRepresentationList.KMeansHSISeg.GMMMMAdapter.rst
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.cp+pc.idc -r wang.cp+pc.GMMNNAdapter.rst
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.cp+pc+tp.idc -r wang.cp+pc+tp.GMMNNAdapter.rst
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.cp+tp.idc -r wang.cp+tp.GMMNNAdapter.rst
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.FeatureRepresentationList.DPU_GongProminentColor.idc -r wang.FeatureRepresentationList.DPU_GongProminentColor.GMMNNAdapter.rst.max
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.FeatureRepresentationList.DPU_GongProminentColor.idc -r wang.FeatureRepresentationList.DPU_GongProminentColor.GMMNNAdapter.rst.mean.nolog
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.FeatureRepresentationList.ColorPatchSeg.idc -r wang.FeatureRepresentationList.ColorPatchSeg.GMMNNAdapter.rst.mean.nolog
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.ts+st4.idc -r wang.ts+st4.GMMNNAdapter.rst
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.cs+st4.idc -r wang.cs+st4.GMMNNAdapter.rst
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.cs+ts.idc -r wang.cs+ts.GMMNNAdapter.rst
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis -d wang.cs+ts+st4.idc -r wang.cs+ts+st4.GMMNNAdapter.rst

# classify analysis 2 (use desc instead of idc)
java yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis2 -d img_desc.desc -r written_result.rst

# labelling analysis (shows how many actual labels are in the top-n auto-generated labels)
java yi.iu.imgdb.learn.StatTestRst$LabellingAnalysis -d img_desc.desc -r written_result.rst -n top_n

# extract the results on specified objects
java yi.iu.imgdb.learn.StatTestRst$ExtractOnObjects -r VACE1.cs+ts+st.GMMNNAdapter.rst -o aeroplane,car,forest,house,paved_road
 
# merge two results
java yi.iu.imgdb.learn.StatTestRst$MergeStatTestRst -r m.rst -m VACE1.cs+stm3.GMMNNAdapter.field.rst -o m1.rst
java yi.iu.imgdb.learn.StatTestRst$MergeStatTestRst -r m1.rst -m m2.rst -o gt.51.ts+cs+st.GMMNNAdapter.rst

# merge more results
java yi.iu.imgdb.learn.StatTestRst$MergeStatTestRstOfObjs -o obj.list -d wang.cp+pc+tp.idc -a yi.iu.imgdb.learn.GMMNNAdapter
java yi.iu.imgdb.learn.StatTestRst$MergeStatTestRstOfObjs -o obj.list -d wang.cp+tp.idc -a yi.iu.imgdb.learn.GMMNNAdapter

# re-copy a result (useful when serialVersionUID is not matched)
java yi.iu.imgdb.learn.StatTestRst$CopyStatTestRst -r gt.ts+cs+st.GMMNNAdapter.rst -o m2.rst

# list object prob for each image
java yi.iu.imgdb.learn.StatTestRst$ListObjectProb -d VACE1.cs+ts+st.idc -r VACE1.best.GMMNNAdapter.rst -t dis_match_T
java yi.iu.imgdb.learn.StatTestRst$ListObjectProb -d gt.51.ts+cs+st.idc -r gt.51.ts+cs+st.GMMNNAdapter.rst -t 2

#===========================================================
#
# see the gmm model
#
#===========================================================
# the mean color of each component for the first fold
java -Xms64m -Xmx512m yi.iu.imgdb.learn.em.MixtureModelAdapter$DemoClusterMeans -e sd.test.bg.ColorPatchSeg.FeatureRepresentationList.GMMAdapter.exp
java -Xms64m -Xmx512m yi.iu.imgdb.learn.em.MixtureModelAdapter$DemoClusterMeans -e sd.test.bg.ModelBasedMRFSegmenter.FeatureRepresentationList.GMMAdapter.exp

# classify image regions
java -Xms64m -Xmx512m yi.iu.imgdb.learn.em.MixtureModelAdapter$DemoClassification -e experiment.exp [-l|-t|-i]
java -Xms64m -Xmx512m yi.iu.imgdb.learn.em.MixtureModelAdapter$DemoClassification -e sd.test.bg.ColorPatchSeg.FeatureRepresentationList.GMMAdapter.exp -i t.list

#===========================================================
#
# see the gmm-nn model
#
#===========================================================
# output the experiment
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d data_center.idc -a yi.iu.imgdb.learn.GMMNNAdapter -o object -e
# print out the model in experiment
java yi.iu.imgdb.learn.StatModelAdapter$Print -m model.mdl
## test on the model
##java yi.iu.imgdb.learn.StatModelAdapter$Test  -m model.mdl -i test_image.list
# see the top candidates on each component
# the top on each component for the first fold
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoGaussianClusterTops -e experiment.exp -n 2 [-l] [-t] [-y]

# the mean color of each component for the first fold
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoGaussianClusterMeans -e experiment.exp
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoGaussianClusterMeans -e sd.mmtest.KMeansHSISeg.FeatureRepresentationList.GMMNNAdapter.beach.exp
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoGaussianClusterMeans -e gt.FeatureRepresentationList.KMeansHSISeg.GMMNNAdapter.cherry_tree.exp
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoGaussianClusterMeans -e gt.FeatureRepresentationList.KMeansHSISeg.GMMNNAdapter.football_field.exp
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoGaussianClusterMeans -e sd.ct1.mm.bg.GMMNNAdapter.exp -d sd.ct1.mm.bg.idc


# nn wights
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoNNParameters -e experiment.exp
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoNNParameters -e sd.mmtest.KMeansHSISeg.FeatureRepresentationList.GMMNNAdapter.beach.exp
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoNNParameters -e gt.FeatureRepresentationList.KMeansHSISeg.GMMNNAdapter.cherry_tree.exp

# classification
java -Xms64m -Xmx512m yi.iu.imgdb.learn.GMMNNAdapter$DemoGMMClassification -e experiment.exp [-l] [-t] [-y]


#===========================================================
#
# generate color points in 3D color space
#
#===========================================================
java yi.iu.imgdb.learn.FeatureUtil$GatherColors -i football.list -f cs
java yi.iu.imgdb.learn.FeatureUtil$PlotColors -i football.list.color.list
java yi.iu.imgdb.learn.FeatureUtil$PlotCIELabColors -i football.list.lab.list
# draw color point for only one image
# the commented code under CIELabLabeledRgnFts.colorDemo(Dimension) do the trick
# just use the common FeatureUtil.Generate


#===========================================================
#
# convert "machine translate" data
#
#===========================================================
java yi.iu.imgdb.learn.MachineTranslation$GenerateDescription > mt.desc
java -Xms64m -Xmx512m yi.iu.imgdb.learn.MachineTranslation$GenerateImgDataCenter


#===========================================================
#
# simplicity image set dealing
#
#===========================================================
# generate desc from list
java yi.iu.imgdb.learn.SimImageSetUtil$List2Desc

# color patch
# 1s / image * 60K = 1000min = 16 hour
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  t.list -f cp
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent t.list -f cp
# texture patch 2s/image * 60K = 2000min = 32hr
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  t.list -f tp
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent t.list -f tp
# Prominent Color 1.5s/image * 60K = 1500min = 25 hr
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  t.list -f pc
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent t.list -f pc
feature: 18M/cat * 600 cat = 10800M (10G)
2.8M/cat * 600 cat = 1700M (1.7G)
# image data center
java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d t.desc -r cp;tp;pc
2.1M/cat * 600 cat = 1200M(1.2G)

# train on categories
java yi.iu.imgdb.learn.SimImageSetUtil$Train -c 0
java yi.iu.imgdb.learn.SimImageSetUtil$Train -c 1

# make test image data center
java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$MakeTestImageDataCenter

# test on categories
java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$Test -c 0
java yi.iu.imgdb.learn.SimImageSetUtil$Test -c 1

# merge testing results
java yi.iu.imgdb.learn.StatTestRst$MergeStatTestRstOfObjs -o obj.list -d t.cp;tp;pc.idc -a yi.iu.imgdb.learn.GMMNNAdapter
# see the results
java yi.iu.imgdb.learn.StatTestRst -d t.cp;tp;pc.idc -r t.cp;tp;pc.GMMNNAdapter.rst

# 
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  sim.list -f cp 2>out
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  sim.list -f tp 2>>out
perl ..\tools\bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate  sim.list -f pc 2>>out
java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$Train -c 0
java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$Test -c 0
java yi.iu.imgdb.learn.StatTestRst$ROCAnalysis -Xms64m -Xmx512m -d sim.desc -r sim.cp;tp;pc.GMMNNAdapter.0.rst
perl ..\tools\bp2.pl yi.iu.imgdb.learn.SimImageSetUtil$Train -c sim.obj.list
perl ..\tools\bp2.pl yi.iu.imgdb.learn.SimImageSetUtil$Test -c sim.obj.list
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatTestRst$MergeStatTestRstOfObjs -o sim.obj.list -d sim.cp;tp;pc.idc -a yi.iu.imgdb.learn.GMMNNAdapter
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatTestRst$ROCAnalysis -d sim.test.desc -r sim.cp;tp;pc.GMMNNAdapter.rst
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatTestRst$ClassificationAnalysis2 -d sim.test.desc -r sim.cp;tp;pc.GMMNNAdapter.rst
java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatTestRst$LabellingAnalysis -d sim.test.desc -r sim.cp;tp;pc.GMMNNAdapter.rst -n 5
#conver the result to the order as img -> highest scored object first
java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$GenerateImgObjScoreMap -d sim.test.desc -r sim.cp;tp;pc.GMMNNAdapter.rst
java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$PrintImgObjScoreMap -r sim.cp;tp;pc.GMMNNAdapter.obj2score.rst
java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$PrintImgCategorizationInHtml -r sim.cp;tp;pc.GMMNNAdapter.obj2score.rst -d sim.test.desc -c sim.dir.desc

# print category description samples
java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$PrintCategoryDescription -c sim.dir.desc
# print out the p_j_k (as in ALIP paper)
java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$CalculateP_j_k -c sim.dir.desc

java -Xms64m -Xmx512m yi.iu.imgdb.learn.SimImageSetUtil$Annotation -r sim.cp;tp;pc.GMMNNAdapter.obj2score.rst -c sim.dir.desc -d sim.test.desc


