@inproceedings{e5ff04176715442882b7cc77e1236968,
title = "Convolutional autoencoder-based color image classification using chroma subsampling in YCbCr space",
abstract = "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.",
keywords = "Computer vision, Convolutional neural network, Image classification, Sparse autoencoder, Unsupervised feature learning",
author = "Zuhe Li and Yangyu Fan and Fengqin Wang",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 8th International Congress on Image and Signal Processing, CISP 2015 ; Conference date: 14-10-2015 Through 16-10-2015",
year = "2016",
month = feb,
day = "16",
doi = "10.1109/CISP.2015.7407903",
language = "英语",
series = "Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "351--355",
editor = "Lipo Wang and Sen Lin and Zhiyong Tao and Bing Zeng and Xiaowei Hui and Liangshan Shao and Jie Liang",
booktitle = "Proceedings - 2015 8th International Congress on Image and Signal Processing, CISP 2015",
}