Feature representation and metric learning are two critical components in person re-identification models. In this paper, we focus on the feature representation and claim that hand-crafted histogram features can be complementary to Convolutional Neural Network (CNN) features. We propose a novel feature extraction model called Feature Fusion Net (FFN) for pedestrian image representation. In FFN, back propagation makes CNN features constrained by the handcrafted features. Utilizing color histogram features (RGB, HSV, YCbCr, Lab and YIQ) and texture features (multi-scale and multi-orientation Gabor features), we get a new deep feature representation that is more discriminative and compact. Experiments on three challenging datasets (VIPeR, CUHK01, PRID450s) validates the effectiveness of our proposal.
Wu et al. REF combine the hand-crafted histogram features and deep features to fine-tune the classification network.
An Enhanced Deep Feature Representation for Person Re-identification
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
2016 IEEE Winter Conference on Applications of Computer Vision (WACV)
Computer Science