Pedestrian Color Naming via Convolutional Neural Network
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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摘要Color serves as an important cue for many computer vision
tasks. Nevertheless, obtaining accurate color description from images
is non-trivial due to varying illumination conditions, view angles, and
surface reflectance. This is especially true for the challenging problem
of pedestrian description in public spaces. We made two contributions
in this study: (1) We contribute a large-scale pedestrian color naming
dataset with 14,213 hand-labeled images. (2) We address the problem
of assigning consistent color name to regions of single object’s surface.
We propose an end-to-end, pixel-to-pixel convolutional neural network
(CNN) for pedestrian color naming. We demonstrate that our Pedestrian
Color Naming CNN (PCN-CNN) is superior over existing approaches
in providing consistent color names on real-world pedestrian images. In
addition, we show the effectiveness of color descriptor extracted from
PCN-CNN in complementing existing descriptors for the task of person
re-identification. Moreover, we discuss a novel application to retrieve
outfit matching and fashion (which could be difficult to be described by
keywords) with just a user-provided color sketch.
著者Zhiyi Cheng, Xiaoxiao Li, Chen Change Loy
會議名稱The 14th European Conference on Computer Vision
會議開始日08.10.2016
會議完結日16.10.2016
會議地點Amsterdam
會議國家/地區荷蘭
會議論文集題名Computer Vision – ACCV 2016
出版年份2016
月份10
出版社Springer
語言美式英語

上次更新時間 2020-19-10 於 03:14