Web Image Search Using Attribute Assisted Re Ranking Model

Information Technology

The massive growth of digital images over the web, required the best image retrieval techniques that can improve the retrieval accuracy of the images. Hence research focus has been shifted from designing of sophisticated algorithms that can reduce the semantic gap between visual features and the richness of human semantics. Hence many image re-ranking technique has been proposed to enhance the text based image results by taking the advantage of visual information contained in the images. But this earlier techniques are based on the low level visual features. Hence the semantic attributes and low level features are exploited simultaneously by using hypergraph re-ranking method. A hypergraph model the relationship between the images by as per its relevance score to order the images. Its simple belief is that visually analogous images should have related ranking scores. This modelling connection among more close samples will be able to domain the robust semantic similarity and thus expedite the great ranking performance.

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