Visual object recognition using local binary patterns and segment-based feature

Visual object recognition is one of the most challenging problems in computer vision, due to both inter-class and intra-class variations. The local appearance-based features, especially SIFT, have gained a big success in such a task because of their great discriminative power. In this paper, we propose to adopt two different kinds of feature to characterize different aspects of object. One is the Local Binary Pattern (LBP) operator which catches texture structure, while the other one is segment-based feature which catches geometric information. The experimental results on PASCAL VOC benchmarks show that the LBP operator can provide complementary information to SIFT, and segment-based feature is mainly effective to rigid objects, which means its usefulness is class-specific. We evaluated our features and approach by participating in PASCAL VOC Challenge 2009 for the very first attempt, and achieved decent results.