DTRF: A physiologically motivated method for image description

Extensive neurophysiological studies have shown that the receptive field plays a significant role in the human visual system. It has various kinds of properties such as orientation-selectivity, correlativity, etc. Motivated by these structural and functional properties, we propose in this paper a novel local image descriptor namely the Discriminative Transform of Receptive Fields (DTRF). Specifically, Receptive Field Patterns (RFP) are defined around each sample pixel and then divided into two kinds of components: RFP-Surround and RFP-Center. The RFP-Surround serves as the basic feature structure, which is extracted based on Local Annular Discrete Cosine Transform (LADCT) algorithm. The RFP-Center is used to pool these local features to simulate the correlative property of receptive field. Experimental results on the standard Oxford data set demonstrate the superiority of DTR-F over the state-of-the-art descriptors under various types of image transformations such as rotation and scaling changes, viewpoint changes, image blurring, JPEG compression, illumination changes, and image noise.