Learning Iteration-wise Generalized Shrinkage-Thresholding Operators for Blind Deconvolution
Learning Invariant Color Features for Person Re-Identiﬁcation
Matching people across multiple camera views known as person reidentification is a challenging problem due to the change in visual appearance caused by varying lighting conditions. The perceivedcolor of the subject appears to be different under different illuminations. Previous works use color as it is or address these challenges by designing color spaces focusing on a specific cue. In this paper, we propose an approach for learning color patterns from pixels sampled from images across two camera views. The intuition behind this work is that, even though varying lighting conditions across views affect the pixel values of the same color, the final representation of a particular color should be stable andinvariant to these variations, i.e., they should be encoded with the same values. We model color featuregeneration as a learning problem by jointly learning a linear transformation and a dictionary to encode pixel values. We also analyze different photometric invariant color spaces as well as popular colorconstancy algorithm for person reidentification. Using color as the only cue, we compare our approach with all the photometric invariant color spaces and show superior performance over all of them. Combining with other learned low-level and high-level features, we obtain promising results in VIPeR,Person Re-ID 2011, and CAVIAR4REID data sets.
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