Person re-identification based on multi-instance multi-label learning

The person re-identification is an active research branch of the computer vision and attracts many researchers study on it. However, because of the variance in viewpoints, illumination, pedestrians' pose and some of other factors, building a robust person re-identification algorithm in open-world is rather challenging. In this paper, we propose two algorithms, indiscriminative patches trim strategy and multi-instance multi-label learning method, to fix the person re-identification in closed short-term surveillance network. There are two main contributions of this paper: first, we define the discriminative region in a person's image. Correspondingly, an adapted canopy-kmeans algorithm is proposed to evaluate the discriminability of image patches which form the discriminative regions. Besides, two strategies, in local image and in global gallery dataset, are proposed to filter out distractions. Finally, the true discriminative patches are employed to compute the similarity between two images, and then used to re-identify pedestrians. Second, we first introduce the multi-instance multi-label learning methods into re-identification, and hence we propose a framework of solving person re-identification by applying MIMLL. We employ two MIMLL methods, the MIMLBOOST and the MIMLSVM, to detect attributes in each image, and those detected attributes contribute to recognize different pedestrians. Experiments on two benchmark datasets show the competitive performance of the two proposed algorithms.