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Local Difference Binary for Ultrafast and Distinctive Feature Description

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2 Author(s)
Xin Yang ; Electr. & Comput. Eng. Dept., Univ. of California, Santa Barbara, Santa Barbara, CA, USA ; Kwang-Ting Tim Cheng

The efficiency and quality of a feature descriptor are critical to the user experience of many computer vision applications. However, the existing descriptors are either too computationally expensive to achieve real-time performance, or not sufficiently distinctive to identify correct matches from a large database with various transformations. In this paper, we propose a highly efficient and distinctive binary descriptor, called local difference binary (LDB). LDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. A multiple-gridding strategy and a salient bit-selection method are applied to capture the distinct patterns of the patch at different spatial granularities. Experimental results demonstrate that compared to the existing state-of-the-art binary descriptors, primarily designed for speed, LDB has similar construction efficiency, while achieving a greater accuracy and faster speed for mobile object recognition and tracking tasks.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:36 ,  Issue: 1 )