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Shared Parts Latent Topic Model for Image Classification
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| Zeitschriftentitel: | Advanced Materials Research |
|---|---|
| Personen und Körperschaften: | |
| In: | Advanced Materials Research, 271-273, 2011, S. 1257-1262 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Trans Tech Publications, Ltd.
|
| Schlagwörter: |
| author_facet |
Yang, Bin Yang, Bin |
|---|---|
| author |
Yang, Bin |
| spellingShingle |
Yang, Bin Advanced Materials Research Shared Parts Latent Topic Model for Image Classification General Engineering |
| author_sort |
yang, bin |
| spelling |
Yang, Bin 1662-8985 Trans Tech Publications, Ltd. General Engineering http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1257 <jats:p>This paper addresses the problem of accurately classifying image categories without any human interaction. A shared parts latent topic model is presented to share mixture components between categories. Different categories share the similar parts which make the model more accurate. As the number of components is unknown and is to be inferred from the train set, the Dirichlet process is introduced into the model to provide a nonparametric prior for the number of mixture components within each category. Gaussian mixture model is adopted to present the object color feature and the Wishart distribution is applied to estimate the parameters of object shape feature. A number of classification experiments are used to verify the success of our model.</jats:p> Shared Parts Latent Topic Model for Image Classification Advanced Materials Research |
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Trans Tech Publications, Ltd., 2011 |
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Trans Tech Publications, Ltd., 2011 |
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1662-8985 |
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2011 |
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Trans Tech Publications, Ltd. |
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Advanced Materials Research |
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49 |
| title |
Shared Parts Latent Topic Model for Image Classification |
| title_unstemmed |
Shared Parts Latent Topic Model for Image Classification |
| title_full |
Shared Parts Latent Topic Model for Image Classification |
| title_fullStr |
Shared Parts Latent Topic Model for Image Classification |
| title_full_unstemmed |
Shared Parts Latent Topic Model for Image Classification |
| title_short |
Shared Parts Latent Topic Model for Image Classification |
| title_sort |
shared parts latent topic model for image classification |
| topic |
General Engineering |
| url |
http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1257 |
| publishDate |
2011 |
| physical |
1257-1262 |
| description |
<jats:p>This paper addresses the problem of accurately classifying image categories without any human interaction. A shared parts latent topic model is presented to share mixture components between categories. Different categories share the similar parts which make the model more accurate. As the number of components is unknown and is to be inferred from the train set, the Dirichlet process is introduced into the model to provide a nonparametric prior for the number of mixture components within each category. Gaussian mixture model is adopted to present the object color feature and the Wishart distribution is applied to estimate the parameters of object shape feature. A number of classification experiments are used to verify the success of our model.</jats:p> |
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| author | Yang, Bin |
| author_facet | Yang, Bin, Yang, Bin |
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| container_start_page | 1257 |
| container_title | Advanced Materials Research |
| container_volume | 271-273 |
| description | <jats:p>This paper addresses the problem of accurately classifying image categories without any human interaction. A shared parts latent topic model is presented to share mixture components between categories. Different categories share the similar parts which make the model more accurate. As the number of components is unknown and is to be inferred from the train set, the Dirichlet process is introduced into the model to provide a nonparametric prior for the number of mixture components within each category. Gaussian mixture model is adopted to present the object color feature and the Wishart distribution is applied to estimate the parameters of object shape feature. A number of classification experiments are used to verify the success of our model.</jats:p> |
| doi_str_mv | 10.4028/www.scientific.net/amr.271-273.1257 |
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| format | ElectronicArticle |
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| imprint | Trans Tech Publications, Ltd., 2011 |
| imprint_str_mv | Trans Tech Publications, Ltd., 2011 |
| institution | DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1 |
| issn | 1662-8985 |
| issn_str_mv | 1662-8985 |
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| physical | 1257-1262 |
| publishDate | 2011 |
| publishDateSort | 2011 |
| publisher | Trans Tech Publications, Ltd. |
| record_format | ai |
| recordtype | ai |
| series | Advanced Materials Research |
| source_id | 49 |
| spelling | Yang, Bin 1662-8985 Trans Tech Publications, Ltd. General Engineering http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1257 <jats:p>This paper addresses the problem of accurately classifying image categories without any human interaction. A shared parts latent topic model is presented to share mixture components between categories. Different categories share the similar parts which make the model more accurate. As the number of components is unknown and is to be inferred from the train set, the Dirichlet process is introduced into the model to provide a nonparametric prior for the number of mixture components within each category. Gaussian mixture model is adopted to present the object color feature and the Wishart distribution is applied to estimate the parameters of object shape feature. A number of classification experiments are used to verify the success of our model.</jats:p> Shared Parts Latent Topic Model for Image Classification Advanced Materials Research |
| spellingShingle | Yang, Bin, Advanced Materials Research, Shared Parts Latent Topic Model for Image Classification, General Engineering |
| title | Shared Parts Latent Topic Model for Image Classification |
| title_full | Shared Parts Latent Topic Model for Image Classification |
| title_fullStr | Shared Parts Latent Topic Model for Image Classification |
| title_full_unstemmed | Shared Parts Latent Topic Model for Image Classification |
| title_short | Shared Parts Latent Topic Model for Image Classification |
| title_sort | shared parts latent topic model for image classification |
| title_unstemmed | Shared Parts Latent Topic Model for Image Classification |
| topic | General Engineering |
| url | http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.1257 |