A Novel Biologically Inspired Structural Model for Feature Correspondence

Feature correspondence is an essential issue in computer science, which could be well formulated by graph matching (GM). However, traditional GM is susceptible to outliers, thus limiting the applications. To address the issue, we present a biologically inspired feature descriptor (BIFD) corresponding to the simple and complex cell layers in primary visual cortex, which shows robust performance against deformations. Furthermore, we propose a novel biologically inspired structural model (BISM) for feature correspondence by fusing the graph structural information and appearance information described by BIFD in the images. The proposed BIFD imitates the cortical pooling operation across multiscale and multiangle cell layers, which makes BISM robust to outliers and distortions. To demonstrate the validity of the proposed method, we evaluate it in feature correspondence tasks on the public databases. The experimental results on synthetic data prove the validity of the proposed method.