Ranking by k-means voting algorithm for similar image retrieval

Authors:
Przemysław Górecki;Krzysztof Sopyła;Paweł Drozda
Affiliations:
Department of Mathematics and Computer Sciences, University of Warmia and Mazury, Olsztyn, Poland;Department of Mathematics and Computer Sciences, University of Warmia and Mazury, Olsztyn, Poland;Department of Mathematics and Computer Sciences, University of Warmia and Mazury, Olsztyn, Poland
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Year:
2012

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Abstract

Recently, the field of CBIR has attracted a lot of attention in the literature. In this paper, the problem of visually similar image retrieval has been investigated. For this task we use the methods derived from the Bag of Visual Words approach, such as Scale Invariant Feature Transform (SIFT) for identifying image keypoints and K-means to build a visual dictionary. To create a ranking of similar images, a novel Ranking by K-means Voting algorithm is proposed. The experimental section shows that our method works well for similar image retrieval. It turned out that our results are more accurate in comparison with a classical similarity measure based on the Euclidean metric in the order of 6% - 15%.