Local Pattern Collocations Using Regional Co-occurrence Factorization

Human vision benefits a lot from pattern collocations in visual activities such as object detection and recognition. Usually, pattern collocations display as the co-occurrences of visual primitives, e.g., colors, gradients, or textons, in neighboring regions. In the past two decades, many sophisticated local feature descriptors have been developed to describe visual primitives, and some of them even take into account the co-occurrence information for improving their discriminative power. However, most of these descriptors only consider feature co-occurrence within a very small neighborhood, e.g., 8-connected or 16-connected area, which would fall short in describing pattern collocations built up by feature co-occurrences in a wider neighborhood. In this paper, we propose to describe local pattern collocations by using a new and general regional co-occurrence approach. In this approach, an input image is first partitioned into a set of homogeneous superpixels. Then, features in each superpixel are extracted by a variety of local feature descriptors, based on which a number of patterns are computed. Finally, pattern co-occurrences within the superpixel and between the neighboring superpixels are calculated and factorized into a final descriptor for local pattern collocation. The proposed regional co-occurrence framework is extensively tested on a wide range of popular shape, color, and texture descriptors in terms of image and object categorizations. The experimental results have shown significant performance improvements by using the proposed framework over the existing popular descriptors.