As re-ranking is a necessary procedure to boost person re-identification (re-ID) performance on large-scale datasets, the diversity of feature becomes crucial to person reID for its importance both on designing pedestrian descriptions and re-ranking based on feature fusion. However, in many circumstances, only one type of pedestrian feature is available. In this paper, we propose a "Divide and use" re-ranking framework for person re-ID. It exploits the diversity from different parts of a high-dimensional feature vector for fusion-based re-ranking, while no other features are accessible. Specifically, given an image, the extracted feature is divided into sub-features. Then the contextual information of each sub-feature is iteratively encoded into a new feature. Finally, the new features from the same image are fused into one vector for re-ranking. Experimental results on two person re-ID benchmarks demonstrate the effectiveness of the proposed framework. Especially, our method outperforms the state-of-the-art on the Market-1501 dataset.
The re-ranking technique @cite_32 is generally used as a post-processing step in various retrieval problems, where the initial ranking list for a query is sorted based on the pairwise similarities between the query and the instances in the database. The re-ranking procedure refines the initial ranking list by taking account of the neighborhood relations among all the instances. A variety of re-ranking algorithms have been developed for object retrieval. In particular, Sparse Contextual Activation (SCA) @cite_19 encodes the neighborhood set into a sparse vector and measures the sample dissimilarity in generalized Jaccard distance. Bai al @cite_3 provide theoretical explanations for diffusion process, which is a popular branch of re-ranking.
@cite_19 @cite_32 @cite_3
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In this paper, we propose an extremely efficient algorithm for visual re-ranking. By considering the original pairwise distance in the contextual space, we develop a feature vector called sparse contextual activation (SCA) that encodes the local distribution of an image. Hence, re-ranking task can be simply accomplished by vector comparison under the generalized Jaccard metric, which has its theoretical meaning in the fuzzy set theory. In order to improve the time efficiency of re-ranking procedure, inverted index is successfully introduced to speed up the computation of generalized Jaccard metric. As a result, the average time cost of re-ranking for a certain query can be controlled within 1 ms. Furthermore, inspired by query expansion, we also develop an additional method called local consistency enhancement on the proposed SCA to improve the retrieval performance in an unsupervised manner. On the other hand, the retrieval performance using a single feature may not be satisfactory enough, which inspires us to fuse multiple complementary features for accurate retrieval. Based on SCA, a robust feature fusion algorithm is exploited that also preserves the characteristic of high time efficiency. We assess our proposed method in various visual re-ranking tasks. Experimental results on Princeton shape benchmark (3D object), WM-SRHEC07 (3D competition), YAEL data set B (face), MPEG-7 data set (shape), and Ukbench data set (image) manifest the effectiveness and efficiency of SCA. The explosive growth and widespread accessibility of community-contributed media content on the Internet have led to a surge of research activity in multimedia search. Approaches that apply text search techniques for multimedia search have achieved limited success as they entirely ignore visual content as a ranking signal. Multimedia search reranking, which reorders visual documents based on multimodal cues to improve initial text-only searches, has received increasing attention in recent years. Such a problem is challenging because the initial search results often have a great deal of noise. Discovering knowledge or visual patterns from such a noisy ranked list to guide the reranking process is difficult. Numerous techniques have been developed for visual search re-ranking. The purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. We conclude with several promising directions for future research.