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An index and retrieval framework integrating perceptive features and semantics for multimedia databases

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Abstract

Typically, in multimedia databases, there exist two kinds of clues for query: perceptive features and semantic classes. In this paper, we propose a novel framework for multimedia databases index and retrieval integrating the perceptive features and semantic classes to improve the speed and the precision of the content-based multimedia retrieval (CBMR). We develop a semantics supervised clustering based index approach (briefly as SSCI): 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, and then an index term is built for each cluster. Especially, the perceptive feature vectors in a cluster are organized adjacently in disk. So the SSCI-based nearest-neighbor (NN) search can be divided into two phases: first, the indexes of all clusters are scanned sequentially to get the candidate clusters with the smallest distances from the query example; second, the original feature vectors within the candidate clusters are visited to get search results. Furthermore, if the results are not satisfied, the SSCI supports an effective relevance feedback (RF) search: users mark the positive and negative samples regarded a cluster as unit instead of a single object; then the Bayesian classifiers on perceptive features and that on semantics are used respectively to adjust retrieval similarity distance. Our experiments show that SSCI-based searching was faster than VA+-based searching; the quality of the search result based on SSCI was better than that of the sequential search in terms of semantics; and a few cycles of the RF by the proposed approach can improve the retrieval precision significantly.

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Notes

  1. An index file in an information retrieval system is a file which stores keys that stand for objects in another file.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Zhiping Shi, Qing He & Zhongzhi Shi

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  1. Zhiping Shi
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  2. Qing He
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  3. Zhongzhi Shi
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Corresponding author

Correspondence to Zhiping Shi.

Additional information

This work is supported by the National Science Foundation of China (No. 60435010), 863 National High-Tech Program (No.2006AA01Z128), National Basic Research Priorities Programme (No. 2007CB311004).

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Shi, Z., He, Q. & Shi, Z. An index and retrieval framework integrating perceptive features and semantics for multimedia databases. Multimed Tools Appl 42, 207–231 (2009). https://doi.org/10.1007/s11042-008-0235-y

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