Evaluating the influence of separated RGB channels with ensemble learning

Zile Deng; Yirui Jiang; Yuanlong Cao; Yugen Yi; Ping Li; Qinghua Liu

doi:10.1117/12.2557176

3 January 2020 Evaluating the influence of separated RGB channels with ensemble learning

Zile Deng, Yirui Jiang, Yuanlong Cao, Yugen Yi, Ping Li, Qinghua Liu

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730A (2020) https://doi.org/10.1117/12.2557176
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Image recognition is of great significance currently, especially the one based on RGB images. Recently, as a substantial amount of RGB based CNNs showing great performance on image recognition problems, the initialization problem sees an increasingly importance as is highly associated with the variance as well as the convergence effect. Even though there exists several prevailing initialization methods, they didn’t consider the difference of contributions from different RGB channels on image recognition. Thus, in this paper, we put forward a proper approach to take the RGB contribution into account. Firstly, we conduct experiments to prove that RGB channels have respectively different contributions upon RGB based image recognition by the means that training one general CNN and three CNNs under datasets seperated from CIFAR-10 by RGB channel. Then, the ensemble of those CNNs shows how similar performance as the general one and also their respective accuracies are recorded as the influence proportion, namely their contribution ratio. As long as the influence proportion is obtained, according to that, we can initialize the weights of convolution filters in order to improve the effect of initialization.

Citation Download Citation

Zile Deng, Yirui Jiang, Yuanlong Cao, Yugen Yi, Ping Li, and Qinghua Liu "Evaluating the influence of separated RGB channels with ensemble learning", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730A (3 January 2020); https://doi.org/10.1117/12.2557176

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