Learning cascaded shared-boost classifiers for part-based object detection
This paper focuses on the problem of detecting a number of different class objects in images. We present a novel part-based model for object detection with cascaded classifiers. The coarse root and fine part classifiers are combined into the model. Different from the existing methods which learn roo...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 23(2014), 4 vom: 13. Apr., Seite 1858-71 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. |
| Zusammenfassung: | This paper focuses on the problem of detecting a number of different class objects in images. We present a novel part-based model for object detection with cascaded classifiers. The coarse root and fine part classifiers are combined into the model. Different from the existing methods which learn root and part classifiers independently, we propose a shared-Boost algorithm to jointly train multiple classifiers. This paper is distinguished by two key contributions. The first is to introduce a new definition of shared features for similar pattern representation among multiple classifiers. Based on this, a shared-Boost algorithm which jointly learns multiple classifiers by reusing the shared feature information is proposed. The second contribution is a method for constructing a discriminatively trained part-based model, which fuses the outputs of cascaded shared-Boost classifiers as high-level features. The proposed shared-Boost-based part model is applied for both rigid and deformable object detection experiments. Compared with the state-of-the-art method, the proposed model can achieve higher or comparable performance. In particular, it can lift up the detection rates in low-resolution images. Also the proposed procedure provides a systematic framework for information reusing among multiple classifiers for part-based object detection |
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| Beschreibung: | Date Completed 03.12.2014 Date Revised 08.05.2014 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2014.2307432 |