The RGFF representational model: a system for the automatically learned partitioning of "visual patterns" in digital images

Abstract

This paper describes a system for the automatically learned partitioning of visual patterns in 2D images, based on sophisticated band-pass filtering with fixed scale and orientation sensitivity. The visual patterns are defined as the features which have the highest degree of alignment in the statistical structure across different frequency bands. The analysis reorganizes the image according to an invariance constraint in statistical structure and consists of three stages: pre-attentive stage, integration stage, and learning stage. The first stage takes the input image and performs filtering with log-Gabor filters. Based on their responses, activated filters which are selectively sensitive to patterns in the image are short listed. In the integration stage, common grounds between several activated sensors are explored. The filtered responses are analyzed through a family of statistics. For any given two activated filters, a distance between them is derived via distances between their statistics. The third stage performs cluster partitioning for learning the subspace of log-Gabor filters needed to partition the image data. The clustering is based on a dissimilarity measure intended to highlight scale and orientation invariance of the responses. The technique is illustrated on real and simulated data sets. Finally, this paper presents a computational visual distinctness measure computed from the image representational model based on visual patterns. Experiments are performed to investigate its relation to distinctness as measured by human observers.