Content-Based Image Retrieval

The goal of this research is to develop the necessary methodology for automated recognition of generic object and concept classes in digital images. The work builds on existing object-recognition techniques in computer vision for low-level feature extraction and designs higher-level relationship and cluster features and a new unified recognition methodology to handle the difficult problem of recognizing classes of objects, instead of particular instances. Local feature representations and global summaries that can be used by general-purpose classifiers are developed. A powerful new hierarchical multiple classifier methodology provides the learning mechanism for automating the development of recognizers for additional objects and concepts. The result of this work is a new generic object recognition paradigm that can immediately be applied to automated or semi-automated indexing of large image databases and is a step forward in object recognition.
(Faculty: Shapiro)