Learning generative visual models from few training examples : An incremental bayesian approach tested on 101 object categories
- Author
- LI, Fei-Fei1 ; FERGUS, Rob2 ; PERONA, Pietro3
[1] Princeton University, 35 Olden St, Princeton, NJ 08540, United States
[2] Oxford University, Parks Road, Oxford OX1 3PJ, United Kingdom
[3] California Institute of Technology, 136-93 Mail Code, Pasadena, CA 91125, United States - Issue title
- Generative model based vision
- Author (monograph)
- PECE, Arthur E. C (Editor)1 2 ; LARSEN, Rasmus (Editor)3
[1] Heimdall Vision, Bjørnsonsvej 29, 2500 Valby, Denmark
[2] Department of Computer Science, University of Copenhagen, Universitetsparken 1, 2100 Copenhagen, Denmark
[3] Informatics and Mathematical Modelling, Technical University of Denmark, Richard Petersens Plads, Building 321, 2800 Kgs. Lyngby, Denmark - Source
Computer vision and image understanding (Print). 2007, Vol 106, Num 1, pp 59-70, 12 p ; ref : 18 ref
- CODEN
- CVIUF4
- ISSN
- 1077-3142
- Scientific domain
- Computer science
- Publisher
- Elsevier, San Diego, CA
- Publication country
- United States
- Document type
- Article
- Language
- English
- Author keyword
- Bayesian model Categorization Generative model Incremental learning Object recognition
- Keyword (fr)
- Algorithme apprentissage Approche probabiliste Catégorisation Classification Estimation Bayes Information a priori Intelligence artificielle Maximum vraisemblance Modélisation Méthode itérative Méthode raffinement Procédé discontinu Production par lot Reconnaissance forme Reconnaissance objet Temps réel Utilisation information Vision ordinateur Apprentissage calcul
- Keyword (en)
- Learning algorithm Probabilistic approach Categorization Classification Bayes estimation Prior information Artificial intelligence Maximum likelihood Modeling Iterative method Refinement method Batch process Batch production Pattern recognition Object recognition Real time Information use Computer vision Computational learning
- Keyword (es)
- Algoritmo aprendizaje Enfoque probabilista Categorización Clasificación Estimación Bayes Información a priori Inteligencia artificial Maxima verosimilitud Modelización Método iterativo Método afinamiento Procedimiento discontínuo Producción por lote Reconocimiento patrón Tiempo real Uso información Visión ordenador Aprendizaje cálculo
- Classification
- Pascal
- 001 Exact sciences and technology / 001D Applied sciences / 001D02 Computer science; control theory; systems / 001D02C Artificial intelligence / 001D02C03 Pattern recognition. Digital image processing. Computational geometry
- Discipline
- Computer science : theoretical automation and systems
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 18720663
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Access to the document
Install on your browser
DOI
10.1016/j.cviu.2005.09.012 
https://dx.doi.org/10.1016/j.cviu.2005.09.012
"O:13:\"PanistOpenUrl\":36:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000idc\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:26:\"10.1016\/j.cviu.2005.09.012\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:127:\"Learning generative visual models from few training examples : An incremental bayesian approach tested on 101 object categories\";s:9:\"\u0000*\u0000jtitle\";s:47:\"Computer vision and image understanding (Print)\";s:9:\"\u0000*\u0000stitle\";s:35:\"Comput. vis. image underst. (Print)\";s:7:\"\u0000*\u0000date\";s:4:\"2007\";s:9:\"\u0000*\u0000volume\";s:3:\"106\";s:8:\"\u0000*\u0000issue\";s:1:\"1\";s:8:\"\u0000*\u0000spage\";s:2:\"59\";s:8:\"\u0000*\u0000epage\";s:2:\"70\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"1077-3142\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:2:\"LI\";s:10:\"\u0000*\u0000aufirst\";s:7:\"Fei-Fei\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:2:{i:0;s:11:\"FERGUS, Rob\";i:1;s:14:\"PERONA, Pietro\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:6:\"CVIUF4\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:29:\"Generative model based vision\";s:8:\"\u0000*\u0000title\";s:127:\"Learning generative visual models from few training examples : An incremental bayesian approach tested on 101 object categories\";s:8:\"\u0000*\u0000place\";s:13:\"San Diego, CA\";s:6:\"\u0000*\u0000pub\";s:8:\"Elsevier\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:2:\"12\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Panist access
"O:12:\"IstexOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:26:\"10.1016\/j.cviu.2005.09.012\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:127:\"Learning generative visual models from few training examples : An incremental bayesian approach tested on 101 object categories\";s:9:\"\u0000*\u0000jtitle\";s:47:\"Computer vision and image understanding (Print)\";s:9:\"\u0000*\u0000stitle\";s:35:\"Comput. vis. image underst. (Print)\";s:7:\"\u0000*\u0000date\";s:4:\"2007\";s:9:\"\u0000*\u0000volume\";s:3:\"106\";s:8:\"\u0000*\u0000issue\";s:1:\"1\";s:8:\"\u0000*\u0000spage\";s:2:\"59\";s:8:\"\u0000*\u0000epage\";s:2:\"70\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"1077-3142\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:2:\"LI\";s:10:\"\u0000*\u0000aufirst\";s:7:\"Fei-Fei\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:2:{i:0;s:11:\"FERGUS, Rob\";i:1;s:14:\"PERONA, Pietro\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:6:\"CVIUF4\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:29:\"Generative model based vision\";s:8:\"\u0000*\u0000title\";s:127:\"Learning generative visual models from few training examples : An incremental bayesian approach tested on 101 object categories\";s:8:\"\u0000*\u0000place\";s:13:\"San Diego, CA\";s:6:\"\u0000*\u0000pub\";s:8:\"Elsevier\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:2:\"12\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Istex access
"O:16:\"UnpaywallOpenUrl\":35:{s:8:\"\u0000*\u0000email\";s:27:\"assistance-portail@inist.fr\";s:6:\"\u0000*\u0000fmt\";N;s:6:\"\u0000*\u0000doi\";s:26:\"10.1016\/j.cviu.2005.09.012\";s:6:\"\u0000*\u0000pii\";N;s:7:\"\u0000*\u0000pmid\";N;s:9:\"\u0000*\u0000atitle\";N;s:9:\"\u0000*\u0000jtitle\";N;s:9:\"\u0000*\u0000stitle\";N;s:7:\"\u0000*\u0000date\";N;s:9:\"\u0000*\u0000volume\";N;s:8:\"\u0000*\u0000issue\";N;s:8:\"\u0000*\u0000spage\";N;s:8:\"\u0000*\u0000epage\";N;s:8:\"\u0000*\u0000pages\";N;s:7:\"\u0000*\u0000issn\";N;s:8:\"\u0000*\u0000eissn\";N;s:9:\"\u0000*\u0000aulast\";N;s:10:\"\u0000*\u0000aufirst\";N;s:9:\"\u0000*\u0000auinit\";N;s:10:\"\u0000*\u0000auinitm\";N;s:5:\"\u0000*\u0000au\";N;s:9:\"\u0000*\u0000aucorp\";N;s:7:\"\u0000*\u0000isbn\";N;s:8:\"\u0000*\u0000coden\";N;s:8:\"\u0000*\u0000genre\";N;s:7:\"\u0000*\u0000part\";N;s:9:\"\u0000*\u0000btitle\";N;s:8:\"\u0000*\u0000title\";N;s:8:\"\u0000*\u0000place\";N;s:6:\"\u0000*\u0000pub\";N;s:10:\"\u0000*\u0000edition\";N;s:9:\"\u0000*\u0000tpages\";N;s:9:\"\u0000*\u0000series\";N;s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";N;}"
Unpaywall access
"O:16:\"EuropePMCOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:26:\"10.1016\/j.cviu.2005.09.012\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:127:\"Learning generative visual models from few training examples : An incremental bayesian approach tested on 101 object categories\";s:9:\"\u0000*\u0000jtitle\";s:47:\"Computer vision and image understanding (Print)\";s:9:\"\u0000*\u0000stitle\";s:35:\"Comput. vis. image underst. (Print)\";s:7:\"\u0000*\u0000date\";s:4:\"2007\";s:9:\"\u0000*\u0000volume\";s:3:\"106\";s:8:\"\u0000*\u0000issue\";s:1:\"1\";s:8:\"\u0000*\u0000spage\";s:2:\"59\";s:8:\"\u0000*\u0000epage\";s:2:\"70\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"1077-3142\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:2:\"LI\";s:10:\"\u0000*\u0000aufirst\";s:7:\"Fei-Fei\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:2:{i:0;s:11:\"FERGUS, Rob\";i:1;s:14:\"PERONA, Pietro\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:6:\"CVIUF4\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:29:\"Generative model based vision\";s:8:\"\u0000*\u0000title\";s:127:\"Learning generative visual models from few training examples : An incremental bayesian approach tested on 101 object categories\";s:8:\"\u0000*\u0000place\";s:13:\"San Diego, CA\";s:6:\"\u0000*\u0000pub\";s:8:\"Elsevier\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:2:\"12\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
Europe PubMed Central access
"O:12:\"ArXivOpenUrl\":35:{s:10:\"\u0000*\u0000openUrl\";N;s:6:\"\u0000*\u0000fmt\";s:7:\"journal\";s:6:\"\u0000*\u0000doi\";s:26:\"10.1016\/j.cviu.2005.09.012\";s:6:\"\u0000*\u0000pii\";s:0:\"\";s:7:\"\u0000*\u0000pmid\";s:0:\"\";s:9:\"\u0000*\u0000atitle\";s:127:\"Learning generative visual models from few training examples : An incremental bayesian approach tested on 101 object categories\";s:9:\"\u0000*\u0000jtitle\";s:47:\"Computer vision and image understanding (Print)\";s:9:\"\u0000*\u0000stitle\";s:35:\"Comput. vis. image underst. (Print)\";s:7:\"\u0000*\u0000date\";s:4:\"2007\";s:9:\"\u0000*\u0000volume\";s:3:\"106\";s:8:\"\u0000*\u0000issue\";s:1:\"1\";s:8:\"\u0000*\u0000spage\";s:2:\"59\";s:8:\"\u0000*\u0000epage\";s:2:\"70\";s:8:\"\u0000*\u0000pages\";s:0:\"\";s:7:\"\u0000*\u0000issn\";s:9:\"1077-3142\";s:8:\"\u0000*\u0000eissn\";s:0:\"\";s:9:\"\u0000*\u0000aulast\";s:2:\"LI\";s:10:\"\u0000*\u0000aufirst\";s:7:\"Fei-Fei\";s:9:\"\u0000*\u0000auinit\";s:0:\"\";s:10:\"\u0000*\u0000auinitm\";s:0:\"\";s:5:\"\u0000*\u0000au\";a:2:{i:0;s:11:\"FERGUS, Rob\";i:1;s:14:\"PERONA, Pietro\";}s:9:\"\u0000*\u0000aucorp\";s:0:\"\";s:7:\"\u0000*\u0000isbn\";s:0:\"\";s:8:\"\u0000*\u0000coden\";s:6:\"CVIUF4\";s:8:\"\u0000*\u0000genre\";s:7:\"article\";s:7:\"\u0000*\u0000part\";s:0:\"\";s:9:\"\u0000*\u0000btitle\";s:29:\"Generative model based vision\";s:8:\"\u0000*\u0000title\";s:127:\"Learning generative visual models from few training examples : An incremental bayesian approach tested on 101 object categories\";s:8:\"\u0000*\u0000place\";s:13:\"San Diego, CA\";s:6:\"\u0000*\u0000pub\";s:8:\"Elsevier\";s:10:\"\u0000*\u0000edition\";s:0:\"\";s:9:\"\u0000*\u0000tpages\";s:2:\"12\";s:9:\"\u0000*\u0000series\";s:0:\"\";s:8:\"\u0000*\u0000proxy\";s:30:\"http:\/\/proxyout.inist.fr:8080\/\";s:12:\"\u0000*\u0000integrite\";b:1;}"
arXiv access
