High-Order Topology Modeling of Visual Words for Image Classification.

Abstract

Modeling relationship between visual words in feature encoding is important in image classification. Recent methods consider this relationship in either image or feature space, and most of them incorporate only pairwise relationship (between visual words). However, in situations involving large variability in images, one cannot capture intrinsic invariance of intra-class images using low-order pairwise relationship. The result is not robust to larger variations in images. In addition, as the number of potential pairings grows exponentially with the number of visual words, the task of learning becomes computationally expensive. To overcome these two limitations, we propose an efficient classification framework that exploits high-order topology of visual words in the feature space, as follows. First, we propose a search algorithm that seeks dependence between the visual words. This dependence is used to construct higher order topology in the feature space. Then, the local features are encoded according to this higher order topology to improve the image classification. Experiments involving four common data sets, namely PASCAL VOC 2007, 15 Scenes, Caltech 101, and UIUC Sport Event, demonstrate that the dependence search significantly improves the efficiency of higher order topological construction, and consequently increases the image classification in all these data sets.