To facilitate the learning of evaluation of pedestrian color naming, we build a new large-scale dataset, named Pedestrian Color Naming (PCN) dataset, which contains 14,213 images, each of which hand-labeled with color label for each pixel. All images in the PCN dataset are obtained from the Market- 1501 dataset .
Method and applications
We approach this problem in a general CRF framework with the unary potentials generated by a deep convolutional network. We based our framework architecture on the VGG16 network  with modifications to fit in our low-resolution inputs.
To explore the effectivenss of our method, we combine the region-level color names generated by PCN-CNN with several existing visual descriptors for the task of person re-identification, and test the performance on the widely used VIPeR dataset .
Moreover, we show the possibility of "retrieve with colors" by our PCN-CNN features.
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