10 April 2018 Adjacent bin stability evaluating for feature description
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106154S (2018) https://doi.org/10.1117/12.2302609
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Recent study improves descriptor performance by accumulating stability votes for all scale pairs to compose the local descriptor. We argue that the stability of a bin depends on the differences across adjacent pairs more than the differences across all scale pairs, and a new local descriptor is composed based on the hypothesis. A series of SIFT descriptors are extracted from multiple scales firstly. Then the difference value of the bin across adjacent scales is calculated, and the stability value of a bin is calculated based on it and accumulated to compose the final descriptor. The performance of the proposed method is evaluated with two popular matching datasets, and compared with other state-of-the-art works. Experimental results show that the proposed method performs satisfactorily.
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Dongdong Nie, Dongdong Nie, Qinyong Ma, Qinyong Ma, } "Adjacent bin stability evaluating for feature description", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106154S (10 April 2018); doi: 10.1117/12.2302609; https://doi.org/10.1117/12.2302609
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