Convolutional autoencoder-based color image classification using chroma subsampling in YCbCr space

Zuhe Li, Yangyu Fan, Fengqin Wang

科研成果: 书/报告/会议事项章节会议稿件同行评审

7 引用 (Scopus)

摘要

We propose a convolutional autoencoder neural network for image classification in YCbCr color space to reduce computational complexity. We first learned local image features from image patches in YCbCr space with a sparse autoencoder and then convolved them with large images to obtain global features. Chrominance components were subsampled before convolution as it is permitted to reduce bandwidth for chrominance components in YCbCr space. We then adopted an algorithm to resize the convolved features in chrominance components by shifting the elements after convolution. Global features were finally fed into a softmax classifier to test the classification accuracy. Experimental results reveal that the convolutional neural network in YCbCr space is able to obtain a reduction of at least 21.6% in time consumption compared to the RGB representation with a slight loss in accuracy.

源语言英语
主期刊名Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015
编辑Lipo Wang, Sen Lin, Zhiyong Tao, Bing Zeng, Xiaowei Hui, Liangshan Shao, Jie Liang
出版商Institute of Electrical and Electronics Engineers Inc.
351-355
页数5
ISBN(电子版)9781467390989
DOI
出版状态已出版 - 16 2月 2016
活动8th International Congress on Image and Signal Processing, CISP 2015 - Shenyang, 中国
期限: 14 10月 201516 10月 2015

出版系列

姓名Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015

会议

会议8th International Congress on Image and Signal Processing, CISP 2015
国家/地区中国
Shenyang
时期14/10/1516/10/15

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