Segmentation Techniques based on Background Subtraction and Supervised Learning: A Comparative Study for Images of Mice and Human Skins
نویسندگان
چکیده
This paper presents a comparison between two image segmentation approaches based on background subtraction and supervised learning. Real images from two important issues, which have been studied by several computer vision research groups, were used in our experiments: namely, sign language interpretation and mouse behavior classification. According to performance measures, such as accurate rate, Jaccard coefficient, Yule coefficient, relative area error, and misclassification error, best results were obtained by background subtraction segmentators using images with complex background, otherwise, segmentators based on support vector machines outperformed when simple background were used. keywords: Mouse image segmentation, human skin segmentation, background subtraction, supervised learning
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