Trish Keaton and Rodney Goodman,
A Compression Framework for Content Analysis,
In IEEE Workshop on Content-based Access of Image and Video
Libraries (CBAIVL'99),
Fort Collins, Colorado, USA, pp. 69-73, June 22 1999.
Trish Keaton and Rodney Goodman,
A Compression Framework for Content Analysis,
In IEEE Workshop on Content-based Access of Image and Video
Libraries (CBAIVL'99),
Fort Collins, Colorado, USA, pp. 69-73, June 22 1999.We present a statistical coding framework that supports content analysis and retrieval in the compressed domain. An unsupervised learning approach based on 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.