Image Search Reranking
Journal Title: INTERNATIONAL JOURNAL OF COMPUTER TRENDS & TECHNOLOGY - Year 2013, Vol 6, Issue 5
Abstract
The existing methods for image search re ranking suffer from the unfaithfulness of the as sumptions under which the text-based images search result. The resulting images contain more irrelevant images. Hence the re ranking concept arises to re rank the retrieved images based on the text around the image and data of data of image and visual feature of image. A number of methods are differentiated for this re-ranking. The high-ranked images are used as noisy data and a k-means algorithm for classification is learned to rectify the ranking further. We are study the affectability of the cross validation method to this training data. The preeminent originality of the overall method is in collecting text/metadata of image and visual features in order to achieve an automatic ranking of the images. Supervision is initiated to learn the model weights offline, previous to reranking process. While model learning needs manual labeling of the results for a some limited queries, the resulting model is query autonomous and therefore applicable to any other query .Examples are given for a selection of other classes like vehicles, animals and other classes.
Authors and Affiliations
V Rajakumar , Vipeen V Bopche
Keywords
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