An enhanced and interpretable feature representation approach to support shape classification from binary images

Abstract

Shape classification from binary images is a challenging task within the computer vision community. Commonly, contour and structural features are computed to describe the objects and code patterns robust against rotation, scaling, and shape deformation. However, current techniques get a high-dimensional feature space decreasing the system performance and the attribute interpretability. Here, we introduce an enhanced and interpretable feature representation approach to support shape classification from binary images. Our method, named EIFR, employs a bag of contour fragments-based feature estimation, intrinsically robust to occlusion and shape deformation. Then, a ReliefF-based feature selection is applied to filter non-discriminative attributes. In turn, a kernel-alignment-based projection is used to measure the feature relevance enhancing the data representation through the matching between a similarity matrix computed from filtered attributes and a kernel matrix built from the shape labels. Attained results on benchmark datasets prove that EIFR improves the curvature-based features’ interpretability and favors the classification performance.