Discriminative sub-categorization
The objective of this work is to learn sub-categories. Rather than casting this as a problem of unsupervised clustering, we investigate a weakly supervised approach using both positive and negative samples of the category. We make the following contributions: (i) we introduce a new model for discrim...
| Main Authors: | , |
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| Format: | Conference item |
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IEEE
2013
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| _version_ | 1797097789402906624 |
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| author | Hoai, M Zisserman, A |
| author_facet | Hoai, M Zisserman, A |
| author_sort | Hoai, M |
| collection | OXFORD |
| description | The objective of this work is to learn sub-categories. Rather than casting this as a problem of unsupervised clustering, we investigate a weakly supervised approach using both positive and negative samples of the category. We make the following contributions: (i) we introduce a new model for discriminative sub-categorization which determines cluster membership for positive samples whilst simultaneously learning a max-margin classifier to separate each cluster from the negative samples, (ii) we show that this model does not suffer from the degenerate cluster problem that afflicts several competing methods (e.g., Latent SVM and Max-Margin Clustering), (iii) we show that the method is able to discover interpretable sub-categories in various datasets. The model is evaluated experimentally over various datasets, and its performance advantages over k-means and Latent SVM are demonstrated. We also stress test the model and show its resilience in discovering sub-categories as the parameters are varied. |
| first_indexed | 2024-03-07T05:00:17Z |
| format | Conference item |
| id | oxford-uuid:d806f8cf-d530-4d83-a291-d674330e3ac6 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T05:00:17Z |
| publishDate | 2013 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:d806f8cf-d530-4d83-a291-d674330e3ac62022-03-27T08:45:23ZDiscriminative sub-categorizationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:d806f8cf-d530-4d83-a291-d674330e3ac6Symplectic Elements at OxfordIEEE2013Hoai, MZisserman, AThe objective of this work is to learn sub-categories. Rather than casting this as a problem of unsupervised clustering, we investigate a weakly supervised approach using both positive and negative samples of the category. We make the following contributions: (i) we introduce a new model for discriminative sub-categorization which determines cluster membership for positive samples whilst simultaneously learning a max-margin classifier to separate each cluster from the negative samples, (ii) we show that this model does not suffer from the degenerate cluster problem that afflicts several competing methods (e.g., Latent SVM and Max-Margin Clustering), (iii) we show that the method is able to discover interpretable sub-categories in various datasets. The model is evaluated experimentally over various datasets, and its performance advantages over k-means and Latent SVM are demonstrated. We also stress test the model and show its resilience in discovering sub-categories as the parameters are varied. |
| spellingShingle | Hoai, M Zisserman, A Discriminative sub-categorization |
| title | Discriminative sub-categorization |
| title_full | Discriminative sub-categorization |
| title_fullStr | Discriminative sub-categorization |
| title_full_unstemmed | Discriminative sub-categorization |
| title_short | Discriminative sub-categorization |
| title_sort | discriminative sub categorization |
| work_keys_str_mv | AT hoaim discriminativesubcategorization AT zissermana discriminativesubcategorization |