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Assemble New Object Detector With Few Examples.

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Název: Assemble New Object Detector With Few Examples.
Autoři: Yang, Kuiyuan1, Wang, Meng2, Hua, Xian-Sheng3, Yan, Shuicheng4, Zhang, Hong-Jiang5
Zdroj: IEEE Transactions on Image Processing. Dec2011, Vol. 20 Issue 12, p3341-3349. 9p.
Témata: *ALGORITHMS, *PERFORMANCE evaluation, DETECTORS, PASCAL (Computer program language), COMPUTER vision, EXPERIMENTS, ITERATIVE methods (Mathematics)
Abstrakt: Learning a satisfactory object detector generally requires sufficient training data to cover the most variations of the object. In this paper, we show that the performance of object detector is severely degraded when training examples are limited. We propose an approach to handle this issue by exploring a set of pretrained auxiliary detectors for other categories. By mining the global and local relationships between the target object category and auxiliary objects, a robust detector can be learned with very few training examples. We adopt the deformable part model proposed by Felzenszwalb and simultaneously explore the root and part filters in the auxiliary object detectors under the guidance of the few training examples from the target object category. An iterative solution is introduced for such a process. The extensive experiments on the PASCAL VOC 2007 challenge data set show the encouraging performance of the new detector assembled from those related auxiliary detectors. [ABSTRACT FROM AUTHOR]
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Abstrakt:Learning a satisfactory object detector generally requires sufficient training data to cover the most variations of the object. In this paper, we show that the performance of object detector is severely degraded when training examples are limited. We propose an approach to handle this issue by exploring a set of pretrained auxiliary detectors for other categories. By mining the global and local relationships between the target object category and auxiliary objects, a robust detector can be learned with very few training examples. We adopt the deformable part model proposed by Felzenszwalb and simultaneously explore the root and part filters in the auxiliary object detectors under the guidance of the few training examples from the target object category. An iterative solution is introduced for such a process. The extensive experiments on the PASCAL VOC 2007 challenge data set show the encouraging performance of the new detector assembled from those related auxiliary detectors. [ABSTRACT FROM AUTHOR]
ISSN:10577149
DOI:10.1109/TIP.2011.2158231