Illumination Robust Color Naming via Label Propagation

Yuanliu liu, Zejian Yuan, Badong Chen, Jianru Xue, Nanning Zheng; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 621-629


Color composition is an important property for many computer vision tasks like image retrieval and object classification. In this paper we address the problem of inferring the color composition of the intrinsic reflectance of objects, where the shadows and highlights may change the observed color dramatically. We achieve this through color label propagation without recovering the intrinsic reflectance beforehand. Specifically, the color labels are propagated between regions sharing the same reflectance, and the direction of propagation is promoted to be from regions under full illumination and normal view angles to abnormal regions. We detect shadowed and highlighted regions as well as pairs of regions that have similar reflectance. A joint inference process is adopted to trim the inconsistent identities and connections. For evaluation we collect three datasets of images under noticeable highlights and shadows. Experimental results show that our model can effectively describe the color composition of real-world images.

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author = {liu, Yuanliu and Yuan, Zejian and Chen, Badong and Xue, Jianru and Zheng, Nanning},
title = {Illumination Robust Color Naming via Label Propagation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}