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EnglishSignature-based matching has been the dominant choice for state-of-the-art person re-identification across multiple disjoint cameras. An approach that exploits image dissimilarities is proposed, treating re-identification as a binary classification problem. To achieve the objective, the person re-identification problem is addressed as follows: (i) first, compute the image dissimilarity between a pair of images acquired from two disjoint cameras; (ii) then learn the linear subspace where the image dissimilarities lie in an unsupervised fashion and (iii) lastly train a binary classifier in the linear subspace to discriminate between image dissimilarities computed for a positive pair (images are for the same person) and a negative pair (images are for different persons). An approach on two publicly available benchmark datasets is evaluated and compared with state-of-the-art methods for person re-identification. © The Institution of Engineering and Technology 2014.
Person re-identification by modelling principal component analysis coefficients of image dissimilarities / Martinel, Niki; Micheloni, Christian. - In: ELECTRONICS LETTERS. - ISSN 0013-5194. - ELETTRONICO. - 50:14(2014), pp. 1000-1001.
| Titolo: | Person re-identification by modelling principal component analysis coefficients of image dissimilarities |
| Autori: | |
| Data di pubblicazione: | 2014 |
| Rivista: | |
| Citazione: | Person re-identification by modelling principal component analysis coefficients of image dissimilarities / Martinel, Niki; Micheloni, Christian. - In: ELECTRONICS LETTERS. - ISSN 0013-5194. - ELETTRONICO. - 50:14(2014), pp. 1000-1001. |
| Abstract: | Signature-based matching has been the dominant choice for state-of-the-art person re-identification across multiple disjoint cameras. An approach that exploits image dissimilarities is proposed, treating re-identification as a binary classification problem. To achieve the objective, the person re-identification problem is addressed as follows: (i) first, compute the image dissimilarity between a pair of images acquired from two disjoint cameras; (ii) then learn the linear subspace where the image dissimilarities lie in an unsupervised fashion and (iii) lastly train a binary classifier in the linear subspace to discriminate between image dissimilarities computed for a positive pair (images are for the same person) and a negative pair (images are for different persons). An approach on two publicly available benchmark datasets is evaluated and compared with state-of-the-art methods for person re-identification. © The Institution of Engineering and Technology 2014. |
| Handle: | http://hdl.handle.net/11390/1036553 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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