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Title: Support Vector Machine Based Semantic Gap Bridging In Vertical Image Scheme


Support Vector Machine Based Semantic Gap Bridging In Vertical Image Scheme
Author Name:
Mr vijay a tathe ,Prof Ravi Chaure Dept of Computer,PK Technical Campus
Improving the performance of content based image retrieval system has been a challenging problem in front of research community. However semantic gap in CBIR is still the concern while developing CBIR system. In this paper we present a support vector machine (SVM) based visual feature technique for reducing semantic gap in content based image retrieval. In our method visual feature weighting is done using support machine for effective image retrieval. In this paper, we present a vertical search engine, namely iLike, which integrates both text and visual features to improve image retrieval performance. In the vertical search, we have a better chance to integrate visual and textual features: first, text contexts are better organized; hence, focused crawlers/parsers are able to identify patterns and link text descriptions and images with higher confidence. With domain knowledge, we can select image features and similarity measures that are more effective for the domain. Finally, computation issue becomes less critical for a smaller data set. We have implemented iLike as a vertical product search engine for apparels shopping, where textual and visual contents coexist and correlate. In iLike, we discover the relationships between textual features extracted from product descriptions and image features extracted from product pictures. We further associate both types of features to build a bridge across the semantic gap.