Person Re-Identification Using Kernel-Based Metric Learning Methods

Xiong, Fei; Gou, Mengran; Camps, Octavia; Sznaier, Mario

doi:10.1007/978-3-319-10584-0_1

Cited by 460 publications

(531 citation statements)

References 32 publications

Supporting

Mentioning

523

Contrasting

Order By: Relevance

“…Feature Extraction. RGB, HSV, LAB, YCbCr, and SCNCD histograms are extracted according to similar settings in [45] using 32 bins per channel, and settings in [12], respectively. Then, all five features are concatenated together.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Impostor Resilient Multimodal Metric Learning for Person Reidentification

Syed

Han

et al. 2018

Advances in Multimedia

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

“…where and are CCA reduced feature of person and , while ini is a globally learned metric with feature using K-LFDA [45]. We have used linear kernel to save memory and computational time.…”

Section: Cross Views Impostors (Cvi)mentioning

confidence: 99%

Impostor Resilient Multimodal Metric Learning for Person Reidentification

Syed

Han

et al. 2018

Advances in Multimedia

View full text Add to dashboard Cite

show abstract

“…Unsupervised/semisupervised approaches include SDALF [10], eSDC [13], TSR [36], SSCDL [37], Null-semi [38], and fully supervised baselines including KISSME [24], kLDFA [39], DeepNN [15], Null Space [38], and XQDA [40]. Semisupervised person reidentification usually assumes the availability of one-third of the training set, while the whole training set of fully supervised approaches is labelled and adopted in learning procedure.…”

Section: Experiments On Vipermentioning

confidence: 99%

“…We compare the state-of-the-art semisupervised baselines kCCA [41], kLFDA [39], XQDA [40], and Null-semi [38] on PRID2011 with access to the implementation codes using the same LOMO features. It can be seen that (see Table 2) (1) except result at rank@10, rank@1, and rank@5, matching rate of our method is the best result compared with baselines, and there is only 0.2% margin below Null-semi that takes the best performance at rank@10.…”

Section: Experiments On Prid2011mentioning

confidence: 99%

Co-Metric Learning for Person Re-Identification

Leng

2018

Advances in Multimedia

View full text Add to dashboard Cite

Person re-identification, aiming to identify the same pedestrian images across disjoint camera views, is a key technique of intelligent video surveillance. Although existing methods have developed both theories and experimental results, most of effective ones pertain to fully supervised training styles, which suffer the small sample size (SSS) problem a lot, especially in label-insufficient practical applications. To bridge SSS problem and learning model with small labels, a novel semisupervised co-metric learning framework is proposed to learn a discriminative Mahalanobis-like distance matrix for label-insufficient person re-identification. Different from typical co-training task that contains multiview data originally, single-view person images are firstly decomposed into pseudo two views, and then metric learning models are produced and jointly updated based on both pseudo-labels and references iteratively. Experiments carried out on three representative person re-identification datasets show that the proposed method performs better than state of the art and possesses low label sensitivity.

show abstract

“…By utilizing information from existing datasets, an adaptive metric learning method is further introduced to strength the multi-modal distribution properties [11]. In the single-shot re-identification field, the relative distance comparison based metric learning [13], kernel based metric learning methods [14], and deep neural network based deep metric [15] are also discussed and presented. Despite a substantial amount of effort, the inter-/intra-class variance issue remains as a great challenge.…”

Section: Copyright Cmentioning

confidence: 99%

Multiple-Shot People Re-Identification by Patch-Wise Learning

Zhang

Kato

Wang

et al. 2015

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARY In this paper, we propose a patch-wise learning based approach to deal with the multiple-shot people re-identification task. In the proposed approach, re-identification is formulated as a patch-wise set-toset matching problem, with each patch set being matched using a specifically learned Mahalanobis distance metric. The proposed approach has two advantages: (1) a patch-wise representation that moderates the ambiguousness of a non-rigid matching problem (of human body) to an approximate rigid one (of body parts); (2) a patch-wise learning algorithm that enables more constraints to be included in the learning process and results in distance metrics of high quality. We evaluate the proposed approach on popular benchmark datasets and confirm its competitive performance compared to the state-of-the-art methods.

show abstract

Person Re-Identification Using Kernel-Based Metric Learning Methods

Cited by 460 publications

References 32 publications

Impostor Resilient Multimodal Metric Learning for Person Reidentification

Impostor Resilient Multimodal Metric Learning for Person Reidentification

Co-Metric Learning for Person Re-Identification

Multiple-Shot People Re-Identification by Patch-Wise Learning

Contact Info

Product

Resources

About