Abstract Person re‐identification addresses the problem of pedestrian image matching in a non‐overlapped surveillance network. Traditional metric learning methods try to learn a fixed pedestrian images matching metric model. However, existing metric learning‐based methods have the problem of overfitting the training data. In order to solve this problem, a sample‐specific metric learning‐based method is proposed. In the perspective of probability distribution, the over‐fitting problem is attributed to the problem that the generalisation ability of projection features is weak. Firstly, the proposed method learns a metric subspace, which projects the raw feature to a discriminative subspace. Then, a projection feature selection method is established based on the probability distribution of positive image pairs. According to the proposed method, the probability that the test samples following the training data distribution is closely related to the adaptability. The proposed projection feature selection method selects different projection features for each individual's similarity distance measure. Finally, extensive experiments on four published datasets verify the effectiveness of the proposed metric learning method. It performs favourably against the comparison methods, especially on the rank‐1 rate.

Person reidentification is a hot topic in the computer vision field. Many efforts have been paid on modeling a discriminative distance metric. However, existing metric-learning-based methods are a lack of generalization. In this article, the poor generalization of the metric model is argued as the biased estimation problem that the independent identical distribution hypothesis is not valid. The verification experimental result shows that there is a sharp difference between the training and test samples in the metric subspace. A semisupervised consistent projection metric-learning method is proposed to ease the biased estimation problem by learning a consistent constrained metric subspace in which the identified pairs are forced to follow the distribution of the positive training pairs. First, a semisupervised method is proposed to generate potential matching pairs from the k -nearest neighbors of test samples. The potential matching pairs are used to estimate the distances' distribution center of the positive test pairs. Second, the metric subspace is improved by forcing this estimation to be close to the center of the positive training pairs. Finally, extensive experiments are conducted on five datasets and the results demonstrate that the proposed method reaches the best performance, especially on the rank-1 identification rate.

Fine-grained visual categorization (FGVC) refers to assigning fine-grained labels to images which belong to the same base category. Due to the high inter-class similarity, it is challenging to distinguish fine-grained images under different subcategories. Recently, researchers have proposed to firstly localize key object parts within images and then find discriminative clues on object parts. To localize object parts, existing methods train detectors for different kinds of object parts. However, due to the fact that the same kind of object part in different images often changes intensely in appearance, the existing methods face two shortages: 1) Training part detector for object parts with diverse appearance is laborious; 2) Discriminative parts with unusual appearance may be neglected by the trained part detectors. To localize the key object parts efficiently and accurately, a novel FGVC method is proposed in the paper. The main novelty is that the proposed method localizes the key object parts within each image only depending on a single image and hence avoid the influence of diversity between parts in different images. The proposed FGVC method consists of two key steps. Firstly, the proposed method localizes the key parts in each image independently. To this end, potential object parts in each image are identified and then these potential parts are merged to generate the final representative object parts. Secondly, two kinds of features are extracted for simultaneously describing the discriminative clues within each part and the relationship between object parts. In addition, a part based dropout learning technique is adopted to boost the classification performance further in the paper. The proposed method is evaluated in comparison experiments and the experiment results show that the proposed method can achieve comparable or better performance than state-of-the-art methods.

Person re-identification problem is targeting to match people in the views of non-overlapped camera networks. It is an important task in the fields of computer vision and video surveillance. It shows great value in applications like surveillance and action recognition. Existing metric learning based methods measure the similarity of sample pairs by learning a metric space in which the positive pairs are closer than negative pairs. However, the appearance features undergo with drastic variation. Person re-identification is a typical small sample problem. It is hard to learn a robust projection of metric subspace that takes all the situations into consideration. The learned metric subspace is usually over-fitting to training dataset due to the strict metric learning constraint . And the hard pairs in training dataset will weaken the discrimination of matching pairs’ similarity. To address these problems, a feedback mechanism based iterative metric learning method is proposed. The proposed method introduces a mean distance of multi-metric subspace to deal with the over-fitting problem. The joint discriminant optimal model on feedback top ranks matching pairs will enhance the discrimination of matching pairs’ similarity. It is a robust and discriminative distance metric which measures the matching pairs similarity with distances of multiple metric projections learned by a set of training datasets. Aiming to learn the multi-metric subspace, the proposed method gives a feedback mechanism based approach which back propagates the top ranks identification results as pseudo training datasets. The effectiveness of proposed mean distance of multi-metric projection is analyzed and proved theoretically. And extensive experiments on three challenging datasets, VIPeR, GRID and CUHK01 are conducted. The results show that the proposed method achieves the best performance and improves the state-of-the-art rank-1 identification rates by 18.48%, 2.00% and 5.41% on three datasets respectively.