Capturing correlations of local features for image representation

Abstract

Local descriptors are popular ways to characterize the local properties of images in various computer vision based tasks. To form the global descriptors for image regions, the first-order feature pooling is widely used. However, as the first-order pooling technique treats each dimension of local features separately, the pairwise correlations of local features are usually ignored.Encouraged by the success of recently developed second-order pooling techniques, in this paper we formulate a general second-order pooling framework and explore several analogues of the second-order average and max operations. We comprehensively investigate a variety of moments which are in the central positions to the second-order pooling technique. As a result, the superiority of the second-order standardized moment average pooling (2Standmap) is suggested. We successfully apply 2Standmap to four challenging tasks namely texture classification, medical image analysis, pain expression recognition, and micro-expression recognition. It illustrates the effectiveness of 2Standmap to capture multiple cues and the generalization to both static images and spatial-temporal sequences.