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Optimal feature space for semantic image segmentation

  • Representation, Processing, Analysis and Understanding of Images
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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.

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Authors and Affiliations

  1. Kogan Research Institute for Neurocybernetics at the Southern Federal University, Stachki Ave., 194/1, Rostov-on-Don, 344090, Russia

    S. I. Anishchenko & M. V. Petrushan

Authors
  1. S. I. Anishchenko
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  2. M. V. Petrushan
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Correspondence to S. I. Anishchenko.

Additional information

This paper uses the materials of the report submitted at the 11th International Conference “Pattern Recognition and Image Analysis: New Information Technologies,” Samara, The Russian Federation, September 23–28, 2013.

Sergei Ivanovich Anishchenko. Born 1982. Graduated from Don State Technical University in 2004. Received degree of Doctor of Philosophy from Middlesex University London in 2013. Researcher at the A.B. Kogan Research Institute for Neurocybernetics at the Southern Federal University. Scientific interests: face image analysis, image segmentation, and detection of feature points. Author of more than 15 papers.

Mikhail Viktorovich Petrushan. Born 1983. Graduated from the Rostov State University in 2006. Researcher at the A.B. Kogan Research Institute for Neurocybernetics at the Southern Federal University. Scientific interests: visual perception, image segmentation, image recognition, robotics, and biometric identification. Author of more than 20 papers. Winner of the competition of innovation projects of students and post-graduate students on “Security and counter-terrorism” and winner of the competition “Computer continuum: from idea to realization” by Intel and Skolkovo Innovation Center.

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Anishchenko, S.I., Petrushan, M.V. Optimal feature space for semantic image segmentation. Pattern Recognit. Image Anal. 24, 502–505 (2014). https://doi.org/10.1134/S1054661814040026

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  • DOI: https://doi.org/10.1134/S1054661814040026

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