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Pixel Club: Improving Large-Scale Image Retrieval using Geometric Weighting
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Dima Sezganov (EE, Technion)
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Tuesday, 5.2.2013, 11:30
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EE Meyer Building 1061
The Bag-Of-Features (BOF) approaches are becoming central in large-scale image retrieval. The standard BOF method is orderless, completely omitting geometric configuration of visual words. The geometrical information is usually involved only in the post-processing spatial verification step usually implemented with the RANdom SAmple Consensus (RANSAC) algorithm. To enable visual search in real-time, RANSAC can be applied only to a relatively small number of top candidates due to its computational requirements. In this work, we propose an alternative method to perform accurate spatial verification with a significantly lower computational cost. Experimental results show that the proposed method outperforms the baseline BOF, and achieves similar performance as RANSAC based spatial verification, despite the major difference in complexity.
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