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A novel local descriptor based on accumulated ranking and averaged bin across multiple scales
Author(s): Qinyong Ma; Dongdong Nie
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Paper Abstract

A novel local descriptor is proposed in this paper. The difference between the values of each bin across two scales are calculated, and sorted in descending order. The index number of a bin in the sorted list is a measure of the stability across two scales. All the index numbers of a bin are accumulated to produce the accumulated ranking of the bin, which is a measure of the stability across all scales. The accumulated ranking forms the first half part of the descriptor. The averaged bin value across multiple scales is calculated as the second half part of the descriptor. Experiments on Fischer dataset and Oxford dataset demonstrate the effectiveness of the proposed descriptor and its superiority to the state-of-the-art descriptors.

Paper Details

Date Published: 10 April 2018
PDF: 5 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151A (10 April 2018); doi: 10.1117/12.2302605
Show Author Affiliations
Qinyong Ma, Yanshan Univ. (China)
Dongdong Nie, Yanshan Univ. (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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