text: Abstract Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21 and 75.64 on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset.
  @cite_36: This paper presents a novel person re-identification framework based on data fusion. The pipeline of the proposed method is composed of two stages. First, a metric learning paradigm is applied on a bunch of distinct feature extractors to produce an ensemble of estimated distance measures, which are subsequently penalized according to their confidence in evidencing the correct matches from the false ones, and averaged as to draw a final decision. Second, the close persons from the gallery are selected based on the previously fused distance estimates, and utilized to build a dictionary as to reconstruct a given probe pattern. Evaluated on benchmark datasets, the proposed framework advances the state-of-the-art by interesting margins. In particular, Rank1 gains amounting to about 12 , 1 , 6 , and 12 , were scored on VIPeR, CAVIAR4REID, iLIDS, and 3DPeS, respectively.
  @cite_9: We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the basis of multiple low-level hand-crafted and high-level visual features. We then formulate two optimization algorithms, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank- @math recognition rates from @math to @math on the iLIDS benchmark, @math to @math on the PRID2011 benchmark, @math to @math on the VIPeR benchmark, @math to @math on the CUHK01 benchmark and @math to @math on the CUHK03 benchmark.
summary: As in the work of @cite_9 , one simple approach to exploit multiple visual features is to build an ensemble of distance functions, in which each distance function is learned using a single feature and the final distance is calculated from a weighted sum of these distance functions. However, the usage of predetermined weights is undesirable as highly discriminative features in one environment might become irrelevant in another one. In their work, a model to learn weights of these distance functions by optimizing the relative distance or by maximizing the average rank-k recognition rate is proposed. @cite_36 proposed a novel re-ranking method based on a fusion scheme that reweights an ensemble of distance metric outcomes according to their discriminative capacity. They particularly show that the fused distance perform largely better than any of the distances inferred by each feature separately.