The Bag-of-Words Methods with Pareto-Fronts for Similar Image Retrieval

This paper presents an algorithm for similar image retrieval which is based on the Bag-of-Words model. In Computer Vision the classic BoW algorithm is mainly used in image classification. Its operation is based on processing of one image, creating a visual words dictionary, and specifying the class to which a query image belongs. In the presented modification of the BoW algorithm two different image feature have been chosen, namely a visual words’ occurrence frequency histogram and a color histogram. As a result, using multi-criteria comparison, which so far has not been used in the BoW algorithms, a set of images similar to a query image is obtained, which is located on the Pareto-optimal non-dominated solutions front.

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