YUV空間中基于稀疏自動編碼器的無監(jiān)督特征學(xué)習(xí)
doi: 10.11999/JEIT150557
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2.
(西北工業(yè)大學(xué)電子信息學(xué)院 西安 710072) ②(鄭州輕工業(yè)學(xué)院計算機與通信工程學(xué)院 鄭州 450002)
基金項目:
陜西省科技統(tǒng)籌創(chuàng)新工程重點實驗室項目(2013SZS15- K02)
Unsupervised Feature Learning with Sparse Autoencoders in YUV Space
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2.
(School of Electronics and Information, Northwestern Polytechnical University, Xi&rsquo
Funds:
The Science and Technology Innovation Engineering Program for Shaanxi Provincial Key Laboratories (2013SZS15-K02)
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摘要: 現(xiàn)有無監(jiān)督特征學(xué)習(xí)算法通常在RGB色彩空間進行特征提取，而圖像和視頻壓縮編碼標準則廣泛采用YUV色彩空間。為了利用人類視覺特性和避免色彩空間轉(zhuǎn)換所消耗的計算量，該文提出一種基于稀疏自動編碼器在YUV色彩空間進行無監(jiān)督特征學(xué)習(xí)的方法。首先在YUV空間隨機采集圖像子塊并進行白化處理，然后利用稀疏自動編碼器進行無監(jiān)督局部特征學(xué)習(xí)。在預(yù)處理階段，針對YUV空間亮度和色度通道相互獨立的特性，提出一種將亮度和色度進行分離的白化措施。最后用學(xué)習(xí)到的局部特征在大尺寸圖像上進行卷積操作從而獲得全局特征，并送入圖像分類系統(tǒng)進行性能測試。實驗結(jié)果表明：只要對亮度分量進行適當(dāng)?shù)陌谆幚恚赮UV空間中的無監(jiān)督特征學(xué)習(xí)就能夠獲得相當(dāng)于甚至優(yōu)于RGB空間的彩色圖像分類性能。
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關(guān)鍵詞:
- 圖像分類 /
- 無監(jiān)督特征學(xué)習(xí) /
- 稀疏自動編碼器 /
- 卷積神經(jīng)網(wǎng)絡(luò) /
- 深度學(xué)習(xí)
Abstract: Existing unsupervised feature learning algorithms usually extract features in RGB color space, but YUV color space is widely adopted in image and video compression standards. In order to take advantage of human visual characteristics and avoid the calculation consumption caused by color space conversion, an unsupervised feature learning approach in YUV space based on sparse autoencoders is presented. First, image patches in YUV space are randomly sampled and whitened, and then are fed into sparse autoencoders to learn local features in an unsupervised way. Considering the characteristic that the luminance channel and chrominance channels are independent in YUV space, a whitening method which treats the luminance and chrominance separately is proposed in the pre-processing step. Finally, features learned over local image patches are convolved with large-size images in order to get global feature activations. Global features are then sent into image classification systems for performance testing. Experimental results reveal that unsupervised feature learning in YUV space achieves similar or even slightly better performance in color image classification compared with that in RGB space as long as the luminance component is whitened properly. -
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