Self-adaptive algorithm for segmenting skin regions

Michal Kawulok,Bogdan Smolka,Jakub Nalepa,Jolanta Kawulok

doi:10.1186/1687-6180-2014-170

Michal Kawulok, Bogdan Smolka + Show 2 more

Open Access

https://doi.org/10.1186/1687-6180-2014-170

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Abstract

In this paper, we introduce a new self-adaptive algorithm for segmenting human skin regions in color images. Skin detection and segmentation is an active research topic, and many solutions have been proposed so far, especially concerning skin tone modeling in various color spaces. Such models are used for pixel-based classification, but its accuracy is limited due to high variance and low specificity of human skin color. In many works, skin model adaptation and spatial analysis were reported to improve the final segmentation outcome; however, little attention has been paid so far to the possibilities of combining these two improvement directions. Our contribution lies in learning a local skin color model on the fly, which is subsequently applied to the image to determine the seeds for the spatial analysis. Furthermore, we also take advantage of textural features for computing local propagation costs that are used in the distance transform. The results of an extensive experimental study confirmed that the new method is highly competitive, especially for extracting the hand regions in color images.

Highlights

Detection and segmentation of human skin regions [1,2] in color images is an active research topic, which receives considerable attention from image and signal processing community
We introduced the discriminative skin-presence features (DSPF) space, which is exploited to compute the local costs for distance transform (DT), instead of using the skin probability map as in [59]
6 Conclusions In this paper, we proposed a new method for creating selfadaptive seeds for spatial-based skin segmentation

Summary

Introduction

Detection and segmentation of human skin regions [1,2] in color images is an active research topic, which receives considerable attention from image and signal processing community. Skin detection consists in taking a binary decision whether an image, its region, or a particular pixel presents the human skin. In case of the positive answer, skin segmentation is applied to determine the exact boundaries of the detected skin regions. 1.2 Contribution In the work reported here, we introduce a new method that consists in combining three important elements, namely, (i) skin color model adaptation, (ii) spatial analysis, and (iii) exploitation of the textural features. The local model is applied to locate the seeds for spatial analysis, which determines the final boundaries of the skin regions. We perform the spatial analysis using the discriminative skin-presence features (DSPF), introduced in our earlier work [9], that rely on textural properties of skin probability maps

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