Recently, one paper on person re-identification is accepted by the journal IEEE TIP. The full name of IEEE TIP is Transactions on Image Processing, which is an international journal on computer vision and image processing with an impact factor of 6.79 announced in 2020. The paper information is as follow:

Furong Xu, Bingpeng Ma, Hong Chang, and Shiguang Shan.“Isosceles Constraints for Person Re-identification”， IEEE Transactions on Image Processing (TIP), 2020. (Accepted)

Person re-identification is a challenging retrieval task. The same person has the great differences due to the different positions, time, etc., and the different persons have the great similarity due to the same view, posture, etc. Batch hard triplet loss (TriHard) has achieved certain success on this task. However, it is difficult to excavate triplets globally. TriHard only focuses on triplets in one batch, instead of the triplets in the global scope. When the distance margin between the matched pair and the mismatched pair is constrained, there is no guarantee that the triplets in the global scope also satisfy the margin constraint. To further constrain the distance of sample pairs in the global range, some methods add a fixed-value distance constraint for the matched pairs or the mismatched pairs. However, using the same fixed-value constraint for all matched/mismatched sample pairs is too strict and can easily lead to the distortion of the optimized feature space. To achieve the purpose of adaptive optimization of the sample pair distance in the global range, this paper proposes an isosceles constraint. The idea of an isosceles constraint is that when two samples are close enough, their distance to any other sample should be as equal as possible. Conversely, if there are multiple samples at equal distances from a certain two samples, then the two samples should be very close with high probability.