Abstract
Semantics-based retrieval is a trend of the Content-Based Multimedia Retrieval (CBMR). Typically, in multimedia databases, there exist two kinds of clues for query: perceptive features and semantic classes. In this chapter, we proposed a novel framework for multimedia database organization and retrieval, integrating the perceptive features and semantic classes. Thereunto, a semantics supervised cluster-based index organization approach (briefly as SSCI) was developed: the entire data set is divided hierarchically into many clusters until the objects within a cluster are not only close in the perceptive feature space, but also within the same semantic class; then an index entry is built for each cluster. Especially, the perceptive feature vectors in a cluster are organized adjacently in disk. Furthermore, the SSCI supports a relevance feedback approach: users sign the positive and negative examples regarded a cluster as unit rather than a single object. Our experiments show that the proposed framework can improve the retrieval speed and precision of the CBMR systems significantly.
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