Unsupervised Salience Learning for Person Re-identification

Zhao, Rui; Ouyang, Wanli; Wang, Xiaogang

doi:10.1109/cvpr.2013.460

Cited by 975 publications

(768 citation statements)

References 26 publications

(60 reference statements)

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“…SDC knn [30] In this method, Zhao et al claim that humans can easily distinguish people by identifying their discriminative features. Hence, they design a method to extract salient features of pedestrian images.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…Then, they proposed a learning approach to search for the optimal representation. Zhao et al [30] focused on extracting discriminative image patches and learning human salient descriptor from them.…”

Section: Related Workmentioning

confidence: 99%

“…In [16], the authors used a deep learning framework to learn pairs of mid-level filters which encode the transformation of mid-level appearance between the two cameras. Inspired by the recognition ability of human being, the authors in [31] proposed an unsupervised method for detecting salient and distinctive local patches and used them for matching images.…”

Section: Related Workmentioning

confidence: 99%

“…In order to verify the effectiveness of the proposed re-ranking method, we select 4 state-of-the-art person reidentification methods: SDALF [6], MidFilter [32], Query Adaptive late Fusion (QAF) [33], and SDC knn [30] for experiments. Given initially ranked lists returned by those methods, we then apply the proposed re-ranking method to the lists.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…In [23], only one person re-identification method, SDALF [6], is evaluated on VIPeR [10]. In this work, we extensively consider four state-of-the-art person re-identification methods including SDALF [6], QAF [33], Mid-level filter [32], and SDC knn [30]. These methods were evaluated on three different benchmark datasets: VIPeR [10], ETHZ [5,26], and CUHK01 [15].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Person re-identification with mutual re-ranking

Nguyen

Ngo

et al. 2017

Vietnam J Comput Sci

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Person re-identification is the problem of identifying people moving across cameras. Traditional approaches deal with this problem by pair-wise matching images recorded from two different cameras. A person in the second camera is identified by comparing his image with images in the first camera, independently of other persons in the second camera. In reality, there are many situations in which multiple persons appear concurrently in the second camera. In this paper, we propose a method for postprocessing re-identification results. The idea is to utilize information of co-occurrence persons for comparing and re-arranging given ranked lists. Experiments conducted on different datasets with several state-of-the-art methods have shown the effectiveness of our post-processing method in improving re-identification accuracy.

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Section: Experimental Settingsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Person re-identification with mutual re-ranking

Nguyen

Ngo

et al. 2017

Vietnam J Comput Sci

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AL‐DDCNN: a distributed crossing semantic gap learning for person re‐identification

Cheng

Zhan

2016

Concurrency and Computation

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SUMMARYBy the reason of the variability of light and pedestrians' appearance, it is hard for a camera to obtain a clear human figure. Person re-identification with different cameras is a difficult visual recognition task. In this paper, a novel approach called attribute learning based on distributed deep convolutional neural network model is proposed to address person re-identification task. It shows how attributes, namely the mid-level medium between classes and features, are obtained automatically and how they are employed to re-identify person with semantics when an author-topic model is used to mapping category. Besides, considering the ability to operate on raw pixel input without the need to design special features, deep convolutional neural network is employed to generate features without supervision for attributes learning model. To overcome the model's weakness in computing speed, parallelized implementations such as distributed parameter manipulation and attributes learning are employed in attribute learning based on distributed deep convolutional neural network model. Experiments show that the proposed approach achieves state-of-the-art recognition performance in the VIPeR data set and is with a good semantic explanation.

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Data‐driven pedestrian re‐identification based on hierarchical semantic representation

Cheng

Tao

et al. 2017

Concurrency and Computation

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Summary Limited number of labeled data of surveillance video causes the training of supervised model for pedestrian re‐identification to be a difficult task. Besides, applications of pedestrian re‐identification in pedestrian retrieving and criminal tracking are limited because of the lack of semantic representation. In this paper, a data‐driven pedestrian re‐identification model based on hierarchical semantic representation is proposed, extracting essential features with unsupervised deep learning model and enhancing the semantic representation of features with hierarchical mid‐level ‘attributes’. Firstly, CNNs, well‐trained with the training process of CAEs, is used to extract features of horizontal blocks segmented from unlabeled pedestrian images. Then, these features are input into corresponding attribute classifiers to judge whether the pedestrian has the attributes. Lastly, with a table of ‘attributes‐classes mapping relations’, final result can be calculated. Under the premise of improving the accuracy of attribute classifier, our qualitative results show its clear advantages over the CHUK02, VIPeR, and i‐LIDS data set. Our proposed method is proved to effectively solve the problem of dependency on labeled data and lack of semantic expression, and it also significantly outperforms the state‐of‐the‐art in terms of accuracy and semanteme.

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Unsupervised Salience Learning for Person Re-identification

Cited by 975 publications

References 26 publications

Person re-identification with mutual re-ranking

Person re-identification with mutual re-ranking

AL‐DDCNN: a distributed crossing semantic gap learning for person re‐identification

Data‐driven pedestrian re‐identification based on hierarchical semantic representation

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