Local Descriptors Encoded by Fisher Vectors for Person Re-identification

Ma, Bingpeng; Su, Yu; Jurie, Frédéric

doi:10.1007/978-3-642-33863-2_41

Cited by 268 publications

(198 citation statements)

References 24 publications

(50 reference statements)

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“…In this paper, we use the mask which is automatically obtained by the method [9] with the parameter settings used in [1]. It is a commonly used mask (or a revised mask) in person re-identification [1,18,23,20]. Color names distribution can be obtained according to Eq.…”

Section: Foreground and Background Based Feature Representation In Imentioning

confidence: 99%

“…[1,4,18,15,13,31,12,7]), which is our main concern in this paper and (2) person matching (e.g. [30,19,28,6,11,5,32,14]).…”

Section: Introductionmentioning

confidence: 99%

“…But color information, in comparison with texture information, seems to be a more important cue due to the fact that in most cases, only low-resolution images can be obtained. Traditional color information such as color histogram, a simple yet effective feature representation, is most widely used in [1,4,18,15,13,31,12,19,6,11,5,32]. With the consideration of the influence of illumination variations, we calculate color histograms in different color spaces separately and fuse them to make the final feature more robust to illumination changes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Salient Color Names for Person Re-identification

Yang

Yan

et al. 2014

Lecture Notes in Computer Science

387

280

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Abstract.Color naming, which relates colors with color names, can help people with a semantic analysis of images in many computer vision applications. In this paper, we propose a novel salient color names based color descriptor (SCNCD) to describe colors. SCNCD utilizes salient color names to guarantee that a higher probability will be assigned to the color name which is nearer to the color. Based on SCNCD, color distributions over color names in different color spaces are then obtained and fused to generate a feature representation. Moreover, the effect of background information is employed and analyzed for person re-identification. With a simple metric learning method, the proposed approach outperforms the state-of-the-art performance (without user's feedback optimization) on two challenging datasets (VIPeR and PRID 450S). More importantly, the proposed feature can be obtained very fast if we compute SCNCD of each color in advance.

show abstract

Section: Foreground and Background Based Feature Representation In Imentioning

confidence: 99%

“…[1,4,18,15,13,31,12,7]), which is our main concern in this paper and (2) person matching (e.g. [30,19,28,6,11,5,32,14]).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Salient Color Names for Person Re-identification

Yang

Yan

et al. 2014

Lecture Notes in Computer Science

387

280

View full text Add to dashboard Cite

show abstract

“…The key idea of FV [28] is to model the generation process of local descriptors M by a probability density function p(·; θ) with parameters θ. The gradient (i.e., ∇ θ log p(M|θ)) of the log-likelihood with respect to the parameters of the model can describe how that parameter contributes to the generation process of M [29]. We usually model the probability density function by a Gaussian mixture model (GMM) using Maximum Likelihood (ML) estimation.…”

Section: The Fisher Vectormentioning

confidence: 99%

Local Patch Vectors Encoded by Fisher Vectors for Image Classification

et al. 2018

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Abstract:The objective of this work is image classification, whose purpose is to group images into corresponding semantic categories. Four contributions are made as follows: (i) For computational simplicity and efficiency, we directly adopt raw image patch vectors as local descriptors encoded by Fisher vector (FV) subsequently; (ii) For obtaining representative local features within the FV encoding framework, we compare and analyze three typical sampling strategies: random sampling, saliency-based sampling and dense sampling; (iii) In order to embed both global and local spatial information into local features, we construct an improved spatial geometry structure which shows good performance; (iv) For reducing the storage and CPU costs of high dimensional vectors, we adopt a new feature selection method based on supervised mutual information (MI), which chooses features by an importance sorting algorithm. We report experimental results on dataset STL-10. It shows very promising performance with this simple and efficient framework compared to conventional methods.

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“…In [33], the authors used local features and proposed an unsupervised method for determining feature weight for fusion. Local descriptors of pixels are transferred into Fisher Vectors to represent images in [22]. Unlike other image retrieval problems, local features are not commonly used in person re-identification [9,12].…”

Section: Related Workmentioning

confidence: 99%

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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Local Descriptors Encoded by Fisher Vectors for Person Re-identification

Cited by 268 publications

References 24 publications

Salient Color Names for Person Re-identification

Salient Color Names for Person Re-identification

Local Patch Vectors Encoded by Fisher Vectors for Image Classification

Person re-identification with mutual re-ranking

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