A comparative study using contours and skeletons as shape representations for binary image matching

Contours and skeletons are well-known shape representations that embody visual information by using a limited set of object points. Both representations have been applied in various pattern recognition applications, while studies in cognitive science have investigated their roles in human perception. Despite their importance has been shown in the above-mentioned fields, to our knowledge no existing studies have been conducted to compare their performances. Filling this gap, this paper is an empirical study of these two shape representations by comparing their performances over different binary image categories and variations. The image categories include thick, elongated, and nearly thin images. Image variations include addition of noise to the contours, blurring, and size reduction. The comparative evaluation is achieved by resorting to object classification (OC) and content-based image retrieval (CBIR) algorithms and evaluation metrics. The main findings highlight the superiority of contours but the improvements observed when skeletons are used for images with noisy contours.