Combining Image Compression and Classification Using Vector Quantization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Library-Based Coding: A Representation for Efficient Video Compression and Retrieval
DCC '97 Proceedings of the Conference on Data Compression
Weighted universal transform coding: universal image compression with the Karhunen-Loeve transform
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Browsing the environment with the SNAP&TELL wearable computer system
Personal and Ubiquitous Computing
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We present a statistical coding framework that supports content analysis and retrieval in the compressed domain. An unsupervised learning approach based upon latent variable modeling is adopted to learn a collection, or mixture, of local linear subspaces that are designed for compression, while providing a probabilistic model of the source useful for inferring image content. The compressed bitstream is organized to enable the progressive decoding of the compressed data, such that the bitstream is only decompressed up to the level necessary to satisfy the query. We describe methods of extracting relevant features from the compressed representation that support query based on single and multiple example images, high level class categories such as people, and low-level features like particular colors and textures. Retrieval experiments have shown that this representation provides good inferencing with very little decompression.