A shape comparison reinforcement method based on feature extractors and F1-Score

Evaluating object segmentation is a topic of great interest for shape comparison techniques. In this work, ad-hoc metrics for a detailed segmentation analysis and a novel keypoint based method for comparing pairs of shapes are presented. As references, two different segmentation approaches were used: a handmade segmentation and an automatic one based on a Convolutional Neural Network (CNN). The proposed comparison approach consists of a combination between a keypoint extractor and an invariant scale shape identifier. The overall validation process is established according to different steps, which allow to measure the similarity between shapes. First, Reinforced Matched (RM) and Reinforced Ratio (RR) strategies are implemented. Moreover, five different state-of-the-art keypoint extractors are compared, i.e., SIFT, SURF, ORB, A-KAZE, and BRISK. Experimental tests were performed on a popular collection of images, i.e., the Berkeley Segmentation Dataset and Benchmark 300 (BSDS300), which contains shapes segmented both manually and automatically. The experimental results have shown the effectiveness of the proposed method.