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Efficient Semantic Indexing for Image Retrieval

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Efficient Semantic Indexing for Image Retrieval
Semantic analysis of a document collection can be viewed as an unsupervised clustering of the constituent words and documents around hidden or latent concepts. This has shown to improve the performance of visual bag of words in image retrieval. However, the enhancement in performance depends heavily on the right choice of number of semantic concepts. Most of the semantic indexing schemes are also computationally costly. In this paper, we propose a bipartite graph model (BGM) for image retrieval. BGM is a scalable datastructure that aids semantic indexing in an efficient manner. It can also be incrementally updated. BGM uses tf-idf values for building a semantic bipartite graph. We also introduce a graph partitioning algorithm that works on the BGM to retrieve semantically relevant images from a database. We demonstrate the properties as well as performance of our semantic indexing scheme through a series of experiments.
Chandrika Pulla, Suman Karthik, C. V. Jawahar
Added 09 Aug 2010
Updated 09 Aug 2010
Type Conference
Year 2010
Where ICPR
Authors Chandrika Pulla, Suman Karthik, C. V. Jawahar
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