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Learning local descriptors with latent hard sample mining
Author(s): Haiguo Gu; Yuehuan Wang
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Paper Abstract

Recent researches on learning local descriptor are mainly based on Convolutional Neural Network (CNN), including loss function, network architecture, sample mining, etc. In this paper, we investigated the existing datasets and difficult sample mining methods for learning local feature descriptor. And we find that the samples in the train set are often not enough for various situations. Because the changes in illumination and view are continuous, but the samples are discrete. In the meanwhile, current sample mining ways only excavate existing data of train set, which is not optimal. So we proposed a new sample mining fashion, called latent hard sample mining, which can utilize potential samples for learning. This method can mitigate the influence of inadequate training samples. We compare our approach to recently introduced convolutional local feature descriptors on Photo-tour and Hpatches dataset with different losses, and demonstrate the advantages of the proposed methods in terms of performance.

Paper Details

Date Published: 3 January 2020
PDF: 7 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137334 (3 January 2020); doi: 10.1117/12.2557193
Show Author Affiliations
Haiguo Gu, Huazhong Univ. of Science and Technology (China)
Yuehuan Wang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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