Probabilistic Retrieval: New Insights and Experimental Results
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
A Compression Framework for Content Analysis
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
A database centric view of semantic image annotation and retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lightweight probabilistic texture retrieval
IEEE Transactions on Image Processing
An examplar-based approach for texture compaction synthesis and retrieval
IEEE Transactions on Image Processing
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The ubiquity of networking and computational capacity associated with the new communications media unveil a universe of new requirements for image representation. Among such requirements is the ability of the representation used for coding to support higher-level tasks such as content-based retrieval. We explore the relationships between probabilistic modeling and data compression to introduce a representation-library-based coding-which, by enabling retrieval in the compressed domain, satisfies this requirement. Because it contains an embedded probabilistic description of the source, this new representation allows the construction of good inference models without compromise of compression efficiency, leads to very efficient procedures for query and retrieval, and provides a framework for higher level tasks such as the analysis and classification of video shots.