Similarity learning on an explicit polynomial kernel feature map for person re-identification

Chen, Dapeng; Yuan, Zejian; Hua, Gang; Zheng, Nanning; Wang, Jingdong

doi:10.1109/cvpr.2015.7298764

Cited by 174 publications

(85 citation statements)

References 26 publications

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“…These global metrics [16][17][18][19] project features into low dimension subspace where they tend to maximize the discrimination among different persons; however, these metrics still suffer a great challenge from impostor (an impostor is a person that belongs to the other person and, however, possess higher similarity with the given query than the right Gallery sample) samples [20,21]. Though, in past some attempts are made to eliminate impostors [14,[20][21][22], however, all these attempts have not given due consideration of different transform modals on which the reidentification images lie [23].…”

Section: Introductionmentioning

confidence: 99%

Impostor Resilient Multimodal Metric Learning for Person Reidentification

Syed

Han

et al. 2018

Advances in Multimedia

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In person reidentification distance metric learning suffers a great challenge from impostor persons. Mostly, distance metrics are learned by maximizing the similarity between positive pair against impostors that lie on different transform modals. In addition, these impostors are obtained from Gallery view for query sample only, while the Gallery sample is totally ignored. In real world, a given pair of query and Gallery experience different changes in pose, viewpoint, and lighting. Thus, impostors only from Gallery view can not optimally maximize their similarity. Therefore, to resolve these issues we have proposed an impostor resilient multimodal metric (IRM3). IRM3 is learned for each modal transform in the image space and uses impostors from both Probe and Gallery views to effectively restrict large number of impostors. Learned IRM3 is then evaluated on three benchmark datasets, VIPeR, CUHK01, and CUHK03, and shows significant improvement in performance compared to many previous approaches.

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Section: Introductionmentioning

confidence: 99%

Impostor Resilient Multimodal Metric Learning for Person Reidentification

Syed

Han

et al. 2018

Advances in Multimedia

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“…Broadly re-id literature can be categorized into two themes with one focusing on cleverly designing local features [6], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25] and the other focusing on metric learning [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38]. Typically local feature design aims to find a re-id specific representation based on the some properties among the data in re-id, e.g.…”

Section: Related Workmentioning

confidence: 99%

PRISM: Person Reidentification via Structured Matching

Zhang

Saligrama

2017

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Person re-identification (re-id), an emerging problem in visual surveillance, deals with maintaining entities of individuals whilst they traverse various locations surveilled by a camera network. From a visual perspective re-id is challenging due to significant changes in visual appearance of individuals in cameras with different pose, illumination and calibration. Globally the challenge arises from the need to maintain structurally consistent matches among all the individual entities across different camera views. We propose PRISM, a structured matching method to jointly account for these challenges. We view the global problem as a weighted graph matching problem and estimate edge weights by learning to predict them based on the co-occurrences of visual patterns in the training examples. These co-occurrence based scores in turn account for appearance changes by inferring likely and unlikely visual co-occurrences appearing in training instances. We implement PRISM on single shot and multi-shot scenarios. PRISM uniformly outperforms state-of-the-art in terms of matching rate while being computationally efficient.

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“…Moreover, in [3], L. Zheng et al have designed an unsupervised BOW representation. In order to include geometric constraints, they incorporated the Spatial Pyramid Representation (SPR) [16,45] into the BOW model. They used the colour Names (CN) and HS Histogram (HS) descriptors and employed Multiple Assignment (MA) for each descriptor.…”

Section: A Histogram Encoding Methodsmentioning

confidence: 99%

Person re-ID while Crossing Different Cameras: Combination of Salient-Gaussian Weighted BossaNova and Fisher Vector Encodings

Mejdoub¹,

Ksibi²,

Amar³

et al. 2017

ijacsa

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Abstract-Person re-identification (re-ID) is a challenging task in the camera surveillance field, since it addresses the problem of re-identifying people across multiple non-overlapping cameras. Most of existing approaches have been concentrated on: 1) achieving a robust and effective feature representation; and 2) enforcing discriminative metric learning to predict if two images represent the same identity. In this context, we present a new approach for person re-ID built upon multi-level descriptors. This is achieved by combining three complementary representations: salient-Gaussian Fisher Vector (SGFV) encoding method, salient-Gaussian BossaNova (SGBN) histogram encoding method and deep Convolutional Neural Network (CNN) features. The two first methods adapt the histogram encoding framework to the person re-ID task. This is achieved by integrating the pedestrian saliency map and the spatial location information, in the histogram encoding process. On one hand, human saliency is reliable and distinctive in the person re-ID task, since it can model the uniqueness of the identity. On the other hand, localizing a person in the image can effectively discard noisy background information. Finally, one of the most advanced metric learning in person re-ID: the Cross-view Quadratic Discriminant Analysis (XQDA) is applied on the top of the resulting description. The proposed method yields promising person re-ID results on two challenging image-based person re-ID benchmarks: CUHK03 and Market-1501.

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Similarity learning on an explicit polynomial kernel feature map for person re-identification

Cited by 174 publications

References 26 publications

Impostor Resilient Multimodal Metric Learning for Person Reidentification

Impostor Resilient Multimodal Metric Learning for Person Reidentification

PRISM: Person Reidentification via Structured Matching

Person re-ID while Crossing Different Cameras: Combination of Salient-Gaussian Weighted BossaNova and Fisher Vector Encodings

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