Parallel weighted semantic fusion for cross-media retrieval
by Nidhi Goel; Priti Sehgal
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 4, No. 1, 2015

Abstract: Efficiency and effectiveness of image retrieval (IR) system are based on the good interpretation and integration of multimodal information. Research results in the recent years show that combining the two modalities (text based and content based) even with simple fusion strategies alleviates the image retrieval results and also reduces the semantic gap. In this paper, we deploy parallel computing in weighted semantic similarity technique for IR using both text and content. This technique gives the weightage to the annotations associated with the query image based upon their semantic similarity with user's query and then establishes the semantics with database images. The semantic similarity has been measured using WordNet. For content matching, colour feature is extracted and is represented using fuzzy colour histogram (FCH). Furthermore, to fuse the two modalities, image reordering with late fusion strategy is used. Parallel processing is done at data level using the single program multiple data (SPMD) programming model that focuses on parallel execution of semantic similarity matching computations. The proposed approach shows that the parallel computing largely reduces the response time of the system. Whereas, semantics learned at an early stage helps in reducing the semantic gap. Experiments performed on two standard datasets reveal the good efficiency and effectiveness of the proposed approach.

Online publication date: Sat, 13-Jun-2015

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