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ABSTRACT
This paper adresses the problem of building an index of compressed object data-base. We introduce an informational similarity measure based on coding length of two part codes. Then, we present a methodology to compress the data-base by taking into account inter-object redundancies and by using the informational similarity measure. The method produces an index included in the code of the data-volume. This index is built such that it contains the minimal sufficient information to discriminate the data-volume objects. Then, we present an optimal two part coder for compressing spatio-temporal events contained in Satellite Image Time Series (SITS). The two part coder allows us to measure similarity and, then, to derive an optimal index of SITS spatio-temporal events. The resulting index is representative of the SITS information content and enables queries based on information content.
INDEX TERMS
IIndex Generation, Model classification, Remote sensing, Clustering, Pattern Recognition, Statistical, Time-varying imagery, Models, Compression (Coding), Model-based coding, Texture, Content Analysis and Indexing, Information Search and Retrieval, Information theory
CITATION
Lionel Gueguen, Mihai Datcu, "A Similarity Metric for Retrieval of Compressed Objects: Application for Mining Satellite Image Time Series", IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 562-575, April 2008, doi:10.1109/TKDE.2007.190718
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