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Efficient Retrieval of Images for Search Engine by Visual Similarity and Re Ranking

Author(s): Viswa S S

Journal: International Journal of Advanced Computer Research
ISSN 2249-7277

Volume: 3;
Issue: 10;
Start page: 47;
Date: 2013;
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Keywords: Image search | Image Retrieval | Efficient Image Search | Image Re ranking

Nowadays, web scale image search engines (e.g.Google Image Search, Microsoft Live ImageSearch) rely almost purely on surrounding textfeatures. Users type keywords in hope of finding acertain type of images. The search engine returnsthousands of images ranked by the text keywordsextracted from the surrounding text. However,many of returned images are noisy, disorganized, orirrelevant. Even Google and Microsoft have noVisual Information for searching of images. Usingvisual information to re rank and improve textbased image search results is the idea. Thisimproves the precision of the text based imagesearch ranking by incorporating the informationconveyed by the visual modality.The typicalassumption that the top-images in the text-basedsearch result are equally relevant is relaxed bylinking the relevance of the images to their initialrank positions. Then, a number of images from theinitial search result are employed as the prototypesthat serve to visually represent the query and thatare subsequently used to construct meta re rankers.i.e. The most relevant images are found by visualsimilarity and the average scores are calculated. Byapplying different meta re rankers to an image fromthe initial result, re ranking scores are generated,which are then used to find the new rank positionfor an image in the re ranked search result.Humansupervision is introduced to learn the model weightsoffline, prior to the online re ranking process. Whilemodel learning requires manual labelling of theresults for a few queries, the resulting model isquery independent and therefore applicable to anyother query. The experimental results on arepresentative web image search dataset comprising353 queries demonstrate that the proposed methodoutperforms the existing supervised andunsupervised Re ranking approaches. Moreover, itimproves the performance over the text-based imagesearch engine by morethan 25.48%
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