Geometric feature-based skin image classification

Content-based image classification has always been a hot research topic. This paper aims to propose an efficient image analysis algorithm using geometric features of skin regions to effectively classify images. First, a nonparametric skin color classifier is used to skin detection. Then, the contours of skin regions are constructed using a curve evolution method based on adaptive grids. Finally, the geometric features are extracted from the contours, and the cosine similarity measure is adopted for image classification. The algorithm is tested on a large database consisting of 6000 images. Experimental results illustrate the proposed method perform well in classifying skin images.