Person re-identification via integrating patch-based metric learning and local salience learning

Highlights：

• We propose an extendable re-id framework, which contains two related parts: patch-based metric learning and local salience learning. First, to handle the problem of pose variant, CNN features are extracted to represent the person, and then a light patch-based metric learning method-pLMNN is proposed to enhance the discriminative ability of raw features.

• We propose a Kmeans-based local salience learning algorithm to train the weights of image patches. Meanwhile, a general similarity computation scheme is presented to relieve an existing training problem, i.e., the parameters need to be re-trained for different datasets.

• The experimental results on two challenging datasets demonstrates the effectiveness of our method.