Unsupervised Object Discovery: A Comparison
Identifieur interne : 002698 ( PascalFrancis/Corpus ); précédent : 002697; suivant : 002699Unsupervised Object Discovery: A Comparison
Auteurs : Tinne Tuytelaars ; Christoph H. Lampert ; Matthew B. Blaschko ; Wray BuntineSource :
- International journal of computer vision [ 0920-5691 ] ; 2010.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.
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Pour connaître la documentation sur le format Inist Standard.
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Format Inist (serveur)
NO : | PASCAL 10-0236313 INIST |
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ET : | Unsupervised Object Discovery: A Comparison |
AU : | TUYTELAARS (Tinne); LAMPERT (Christoph H.); BLASCHKO (Matthew B.); BUNTINE (Wray); TRIGGS (Bill); WILLIAMS (Christopher K. I.) |
AF : | ESAT-PSI, K.U. Leuven, Kasteelpark Arenberg 10, bus 2241/Leuven 3001/Belgique (1 aut.); Max Planck Institute for Biological Cybernetics/Tübingen/Allemagne (2 aut., 3 aut.); University of Oxford/Oxford/Royaume-Uni (3 aut.); NICTA/Canberra/Australie (4 aut.); Australian National University/Canberra/Australie (4 aut.); Laboratoire Jean Kuntzman/Grenoble/France (1 aut.); University of Edinburgh/Edinburgh/Royaume-Uni (2 aut.) |
DT : | Publication en série; Niveau analytique |
SO : | International journal of computer vision; ISSN 0920-5691; Allemagne; Da. 2010; Vol. 88; No. 2; Pp. 284-302; Bibl. 3/4 p. |
LA : | Anglais |
EA : | The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed. |
CC : | 001D02C03; 001D02B07B |
FD : | Traitement image; Banque image; Vision ordinateur; Modèle variable latente; Modélisation; Méthode spectrale; Analyse amas; .; Reconnaissance objet; Apprentissage non supervisé |
ED : | Image processing; Image databank; Computer vision; Latent variable model; Modeling; Spectral method; Cluster analysis; Object recognition; Unsupervised learning |
SD : | Procesamiento imagen; Banco imagen; Visión ordenador; Modelo variable latente; Modelización; Método espectral; Analisis cluster; Reconocimiento de objetos; Aprendizaje no supervisado |
LO : | INIST-21361.354000182008660070 |
ID : | 10-0236313 |
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<front><div type="abstract" xml:lang="en">The goal of this paper is to evaluate and compare models and methods for learning to recognize basic entities in images in an unsupervised setting. In other words, we want to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns. We experiment with various baseline methods, methods based on latent variable models, as well as spectral clustering methods. The results are presented and compared both on subsets of Caltech256 and MSRC2, data sets that are larger and more challenging and that include more object classes than what has previously been reported in the literature. A rigorous framework for evaluating unsupervised object discovery methods is proposed.</div>
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