Re-Ranking Of Web Images Using Semantic Signature and Parallel SVM
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 6
Abstract
Image re-ranking has been adopted by current commercial search engines to improve the results of web-based image search. In image re-ranking, for a given a query keyword, first a pool of images are retrieved by the search engine and the user is asked to select a query image from the pool. The remaining images are re-ranked based on their visual similarities with the query image. But there are difficulties in understanding users’ search intention and in learning a universal visual semantic space to characterize the web images. An image re-ranking framework is proposed which automatically offline learns different visual semantic spaces for different query keywords through keyword expansions. The visual features of images are projected into their related visual semantic spaces to get semantic signatures. At the online stage, images are re-ranked by comparing their semantic signatures obtained from the visual semantic space specified by the query keyword. An image can have a single semantic signature or multiple semantic signatures for a specific keyword. Computing multiple semantic signatures for an image has higher precision and re-ranking accuracy than using single semantic signature. But the lengthy combined semantic signatures results in more time consumption. Hence a number of SVMs (Support Vector Machines) can be used in parallel at the online stage which compares multiple semantic signatures at the same time and results are merged at the later stages. Thus images are re-ranked in faster manner while preserving the accuracy
Authors and Affiliations
Nisha Abdul, Kiran V K
Keywords
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