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2017 | OriginalPaper | Buchkapitel

Pedestrian Color Naming via Convolutional Neural Network

verfasst von : Zhiyi Cheng, Xiaoxiao Li, Chen Change Loy

Erschienen in: Computer Vision – ACCV 2016

Verlag: Springer International Publishing

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Abstract

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.

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A basic color term is defined as being not subsumable to other basic color terms and extensively used in different languages.

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Titel: Pedestrian Color Naming via Convolutional Neural Network
verfasst von: Zhiyi Cheng
Xiaoxiao Li
Chen Change Loy
Verlag: Springer International Publishing
Buch: Computer Vision – ACCV 2016
Print ISBN: 978-3-319-54183-9

Electronic ISBN: 978-3-319-54184-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-54184-6_3

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