Encoding Local Binary Descriptors by Bag-of-Features with Hamming Distance for Visual Object Categorization

This paper presents a novel method for encoding local binary descriptors for Visual Object Categorization (VOC). Nowadays, local binary descriptors, e.g. LBP and BRIEF, have become very popular in image matching tasks because of their fast computation and matching using binary bitstrings. However, the bottleneck of applying them in the domain of VOC lies in the high dimensional histograms produced by encoding these binary bitstrings into decimal codes. To solve this problem, we propose to encode local binary bitstrings directly by the Bag-of-Features (BoF) model with Hamming distance. The advantages of this approach are two-fold: (1) It solves the high dimensionality issue of the traditional binary bitstring encoding methods, making local binary descriptors more feasible for the task of VOC, especially when more bits are considered; (2) It is computationally efficient because the Hamming distance, which is very suitable for comparing bitstrings, is based on bitwise XOR operations that can be fast computed on modern CPUs. The proposed method is validated by applying on LBP feature for the purpose of VOC. The experimental results on the PASCAL VOC 2007 benchmark show that our approach effectively improves the recognition accuracy compared to the traditional LBP feature.