Pattern Recognition and Image Analysis

, Volume 24, Issue 4, pp 502–505

Optimal feature space for semantic image segmentation

Representation, Processing, Analysis and Understanding of Images

DOI: 10.1134/S1054661814040026

Cite this article as:
Anishchenko, S.I. & Petrushan, M.V. Pattern Recognit. Image Anal. (2014) 24: 502. doi:10.1134/S1054661814040026
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Abstract

A new method for semantic segmentation with object adaptation is proposed. Segmentation is performed on a feature map obtained as a weighted sum of HSV-space components: hue, saturation, and- value. Homogeneity criterion for grouping pixels into clusters and weighted sum coefficients is adjusted using the particle swarm optimization (PSO) algorithm. The method is tested using images wherein a face is a semantically meaningful object. The accuracy of the segmentation is shown to be higher when using a feature map obtained as a weighted sum of color components than in cases when a single color component is used. The accuracy of the proposed method is estimated as the correspondence of a fragmented segment to a face region, detected using the Viola-Jones method, within an image.

Keywords

semantic segmentation feature spaces optimization 

Copyright information

© Pleiades Publishing, Ltd. 2014

Authors and Affiliations

  1. 1.Kogan Research Institute for Neurocybernetics at the Southern Federal UniversityRostov-on-DonRussia

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