Single-histogram class models for image segmentation
<p>Histograms of visual words (or textons) have proved effective in tasks such as image classification and object class recognition. A common approach is to represent an object class by a set of histograms, each one corresponding to a training exemplar. Classification is then achieved by k-nea...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
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Springer
2006
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| _version_ | 1824458995819085824 |
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| author | Schroff, F Criminisi, A Zisserman, A |
| author_facet | Schroff, F Criminisi, A Zisserman, A |
| author_sort | Schroff, F |
| collection | OXFORD |
| description | <p>Histograms of visual words (or textons) have proved effective in tasks such as image classification and object class recognition. A common approach is to represent an object class by a set of histograms, each one corresponding to a training exemplar. Classification is then achieved by k-nearest neighbour search over the exemplars.</p>
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<p>In this paper we introduce two novelties on this approach: (i) we show that new compact single histogram models estimated optimally from the entire training set achieve an equal or superior classification accuracy. The benefit of the single histograms is that they are much more efficient both in terms of memory and computational resources; and (ii) we show that bag of visual words histograms can provide an accurate pixel-wise segmentation of an image into object class regions. In this manner the compact models of visual object classes give simultaneous segmentation and recognition of image regions.</p>
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<p>The approach is evaluated on the MSRC database [5] and it is shown that performance equals or is superior to previous publications on this database.</p> |
| first_indexed | 2025-02-19T04:34:45Z |
| format | Conference item |
| id | oxford-uuid:6ddcae6c-da55-4f7e-985b-6e5a0524c6e3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:34:45Z |
| publishDate | 2006 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:6ddcae6c-da55-4f7e-985b-6e5a0524c6e32025-01-24T12:25:26ZSingle-histogram class models for image segmentationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:6ddcae6c-da55-4f7e-985b-6e5a0524c6e3EnglishSymplectic ElementsSpringer2006Schroff, FCriminisi, AZisserman, A<p>Histograms of visual words (or textons) have proved effective in tasks such as image classification and object class recognition. A common approach is to represent an object class by a set of histograms, each one corresponding to a training exemplar. Classification is then achieved by k-nearest neighbour search over the exemplars.</p> <br> <p>In this paper we introduce two novelties on this approach: (i) we show that new compact single histogram models estimated optimally from the entire training set achieve an equal or superior classification accuracy. The benefit of the single histograms is that they are much more efficient both in terms of memory and computational resources; and (ii) we show that bag of visual words histograms can provide an accurate pixel-wise segmentation of an image into object class regions. In this manner the compact models of visual object classes give simultaneous segmentation and recognition of image regions.</p> <br> <p>The approach is evaluated on the MSRC database [5] and it is shown that performance equals or is superior to previous publications on this database.</p> |
| spellingShingle | Schroff, F Criminisi, A Zisserman, A Single-histogram class models for image segmentation |
| title | Single-histogram class models for image segmentation |
| title_full | Single-histogram class models for image segmentation |
| title_fullStr | Single-histogram class models for image segmentation |
| title_full_unstemmed | Single-histogram class models for image segmentation |
| title_short | Single-histogram class models for image segmentation |
| title_sort | single histogram class models for image segmentation |
| work_keys_str_mv | AT schrofff singlehistogramclassmodelsforimagesegmentation AT criminisia singlehistogramclassmodelsforimagesegmentation AT zissermana singlehistogramclassmodelsforimagesegmentation |