Relevance Feedback for Visual Data Retrieval

Abstract

Machine-aided retrieval of multimedia information—image [44], video [170], or audio [195], etc.—is achieved based on representations in the form of descriptors (or feature vectors). Two issues arise: one is the effectiveness of the representation, i.e., to what extent can the meaningful contents of the media be represented in these vectors? The other is the selection of a similarity metric during the retrieval process. This is an important issue because the similarity metric dynamically depends upon the user and the user defined query class, which are unknown a priori. In the following, we focus our attention on the second issue, i.e., the on-line learning problem for content-based multimedia information retrieval.

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