Structural Context for Object Categorization

Abstract

Bag of Words model has been widely used in the task of Object Categorization, and SIFT, computed for interest local regions, has been extracted from the image as the representative features, which can provide robustness and invariance to many kind of image transformation. Even though, they can only capture the local information, while be blind to the large picture of the image. Besides, the same part of different objects(like the head lamp of different cars) may also not able to be identically represented by SIFT and the like. In order to efficiently represent the object category, we design a new local descriptor–structural context, which shares the similar idea as Shape Context, capturing the relationship between current point and the remaining points, which is the extrema from the scale space of the image and can to some extent represent the structural of the image. This newly proposed descriptor can provide more discriminative representation of the object category, being invariant to intra-class difference, scale change, illumination variation, clutter noise, partial occlusion, small range of deformation, rotation and viewpoint change. Experiments on object categorization and image matching have proved the effectiveness of our newly proposed descriptor in describing the images of the same category.