UPDATE (1/20/2005)

See "test.bat.commands" for all the available commands

===============================================================================

Enviroment Setup

* run setup.bat to setup "CLASSPATH".  You need change
  PROJECTPATH, PACKAGEPATH, JBUILDERPATH and PROJECTNAME
  to the correct locations.



Running Commands:

* java yi.iu.imgdb.desc.gui.ImageDescApplication
  ----------------------------------------------
  Construct and modify descriptions (labels) for a image set

* java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f cs
  ---------------------------------------------------------------
  generate Color Segmentations
  
  example:
  java yi.iu.imgdb.learn.FeatureUtil$Generate -i groundtruth/arborgreens/arborgreens01.jpg -f cs
  
  output:
  - rst/ft/groundtruth/arborgreens/arborgreens01.jpg.s256.r128.KMeansHSISeg.CIELabLabeledRgnFts.ser
    segmentation result written in java object format
  - rst/ft/groundtruth/arborgreens/arborgreens01.jpg.s256.r128.KMeansHSISeg.CIELabLabeledRgnFts.cdemo.gif
    segmentation result shown in mean colors
  - rst/ft/groundtruth/arborgreens/arborgreens01.jpg.s256.r128.KMeansHSISeg.CIELabLabeledRgnFts.gdemo.gif
    segmentation result shown in pseudo-colors
    
  batch process:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate set_name.list -f cs -h set_name
  ---------------------------------------------------------------------------------
  
  example:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate t.list -f cs -h t
  
  output:
  - 3 outputs for each indivisual image as described above
  - rst/ft/t.KMeansHSISeg.CIELabLabeledRgnFts.html
    a html file to demonstrate the segmentations

* java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f ts
  ---------------------------------------------------------------
  generate Texture Segmentations
  
  example:
  java yi.iu.imgdb.learn.FeatureUtil$Generate -i groundtruth/arborgreens/arborgreens01.jpg -f ts
  
  output:
  - rst/ft/groundtruth/arborgreens/arborgreens01.jpg.s256.r128.ColorGuidedTextureSegNCC.TextureLabeledRgnFts.ser
    segmentation result written in java object format
  - rst/ft/groundtruth/arborgreens/arborgreens01.jpg.s256.r128.ColorGuidedTextureSegNCC.TextureLabeledRgnFts.gdemo.gif
    segmentation result shown in pseudo-colors
    
  batch process:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate set_name.list -f ts -h set_name
  ---------------------------------------------------------------------------------
  
  example:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate t.list -f ts -h t
  
  output:
  - 2 outputs for each indivisual image as described above
  - rst/ft/t.ColorGuidedTextureSegNCC.TextureLabeledRgnFts.html
    a html file to demonstrate the segmentations

* java yi.iu.imgdb.learn.FeatureUtil$Generate -i image_name -f st
  ---------------------------------------------------------------
  generate Structure features (line clusters)
  
  example:
  java yi.iu.imgdb.learn.FeatureUtil$Generate -i groundtruth/campusinfall/campusinfall14.jpg -f st
  
  output:
  - rst/ft/groundtruth/campusinfall/campusinfall14.jpg.LineStructGenerator.LineClusterSets.ser
    line clustering result written in java object format
  - rst/ft/groundtruth/campusinfall/campusinfall14.jpg.LineStructGenerator.LineClusterSets.cdemo.gif
    line clusters shown in different colors
  - rst/ft/groundtruth/campusinfall/campusinfall14.jpg.LineStructGenerator.LineClusterSets.gdemo.gif
    line clusters shown in different colors and enclosed by polygons
    
  batch process:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate set_name.list -f st -h set_name
  ---------------------------------------------------------------------------------
  
  example:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Generate t.list -f st -h t
  
  output:
  - 3 outputs for each indivisual image as described above
  - rst/ft/t.LineStructGenerator.LineClusterSets.html
    a html file to demonstrate the line clustering

* java yi.iu.imgdb.learn.FeatureUtil$Represent -i image_name -f cs
  ----------------------------------------------------------------
  represent Color Segmentation features
  
  example:
  java yi.iu.imgdb.learn.FeatureUtil$Represent -i groundtruth/arborgreens/arborgreens01.jpg -f cs
  
  output:
  - rst/represent/groundtruth/arborgreens/arborgreens01.jpg.s256.r128.KMeansHSISeg.FeatureRepresentationList.m0.ser
    feature representation result written in java object format
    
  batch process:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f cs -h set_name
  ----------------------------------------------------------------------------------
  
  example:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent t.list -f cs -h t
  
  output:
  - 1 output for each indivisual image as described above
  - rst/represent/t.KMeansHSISeg.FeatureRepresentationList.html
    a html file having links to the original images
    
* java yi.iu.imgdb.learn.FeatureUtil$Represent -i image_name -f ts
  ----------------------------------------------------------------
  represent Texture Segmentation features
  
  example:
  java yi.iu.imgdb.learn.FeatureUtil$Represent -i groundtruth/arborgreens/arborgreens01.jpg -f ts
  
  output:
  - rst/represent/groundtruth/arborgreens/arborgreens01.jpg.s256.r128.ColorGuidedTextureSegNCC.FeatureRepresentationList.m0.ser
    feature representation result written in java object format
    
  batch process:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f ts -h set_name
  ----------------------------------------------------------------------------------
  
  example:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent t.list -f ts -h t
  
  output:
  - 1 output for each indivisual image as described above
  - rst/represent/t.ColorGuidedTextureSegNCC.FeatureRepresentationList.html
    a html file having links to the original images

* java yi.iu.imgdb.learn.FeatureUtil$Represent -i image_name -f st
  ----------------------------------------------------------------
  Represent Structure features
  
  example:
  java yi.iu.imgdb.learn.FeatureUtil$Represent -i groundtruth/arborgreens/arborgreens01.jpg -f st
  
  output:
  - rst/represent/groundtruth/arborgreens/arborgreens01.jpg.LineStructGenerator.FeatureRepresentationList.m4.ser
    feature representation result written in java object format
    
  batch process:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent set_name.list -f st -h set_name
  ----------------------------------------------------------------------------------
  
  example:
  perl bp.pl yi.iu.imgdb.learn.FeatureUtil$Represent t.list -f st -h t
  
  output:
  - 1 output for each indivisual image as described above
  - rst/represent/t.LineStructGenerator.FeatureRepresentationList.html
    a html file having links to the original images

* java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d set_name.desc -r representation_desc
  ---------------------------------------------------------------------------------------------
  Construct image data center (image feature database)
  
  example:
  java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d t.desc -r cs
  Generate image data center on image data set described by t.desc 
  and with the color segmentation feature representations.
  
  output:
  - rst/represent/t.KMeansHSISeg.FeatureRepresentationList.idc
    constructed image data center in java object format
  
  example:
  java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d t.desc -r cs;ts;st
  Generate image data center on image data set described by t.desc 
  and with the color segmentation feature representations, texture
  segmentation feature representations, and structure feature
  representations.

  output:
  - rst/represent/t.KMeansHSISeg+ColorGuidedTextureSegNCC+LineStructGenerator.FeatureRepresentationList.idc
    constructed image data center in java object format

  example:
  java -Xms64m -Xmx512m yi.iu.imgdb.learn.ImgDataCenter -d t.desc -r cs;ts;st -o t.cs+ts+st.idc
  Generate image data center on image data set described by t.desc 
  and with the color segmentation feature representations, texture
  segmentation feature representations, and structure feature
  representations.  

  output:
  - rst/represent/t.cs+ts+st.idc
    constructed image data center in java object format with the
    specified name.

* java -Xms64m -Xmx512m yi.iu.imgdb.learn.StatModelFoldExperiment -d data_center.idc -a learn_adapter [-n 5] [-o obj_list] [-r rst_output_file] [-e output_experiment]