Clustering face images
V.B. Nemirovskiy, A.K. Stoyanov

 

Institute of Cybernetics of Tomsk Polytechnic University, Tomsk, Russia

Full text of article: Russian language.

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Abstract:
In this paper a multi-step algorithm for clustering face images is proposed. This algorithm is designed to split a collection of images into groups of similar images. The algorithm is based on clustering the proximity measures between brightness-based segmented images. As proximity measures, the Euclidean distance and the Kullback-Leibler distance were used. Brightness-based image segmentation and clustering respective proximity measures were carried out with the help of a software model of a recurrent neural network. Results of experimental studies of the proposed approach are presented.

Keywords:
image clustering, one-dimensional mapping, neuron, near-duplicate.

Citation:
Nemirovskiy VB, Stoyanov AK. Clustering face images. Computer Optics 2017; 41(1): 59-66. DOI: 10.18287/2412-6179-2017-41-1-59-66.

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