Polish (Poland)English (United Kingdom)

Content-Based Image Retrieval Optimization by Differential Evolution

Type of Publication: In Proceedings
Year: 2016
Book title: Proceedings of 2016 IEEE Congress on Evolutionary Computation (CEC)
Pages: 86-93
Address: Vancouver BC, Canada
In this paper we present a new method for contentbased searching large image databases by comparing content of a query image and images stored in a database. The algorithm consists of three main steps: feature extraction, indexing and system learning. The feature extraction stage is based on two types of features (SURF keypoints and color). For indexing we use the k-means algorithm and for system learning we applied differential evolution. This last step is very important, and significantly improves the results. The presented algorithm can be easily modified, by changing its components (feature extractor or clustering algorithm).

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