Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIP.2010.2103951
Title: Transferring boosted detectors towards viewpoint and scene adaptiveness
Authors: Pang, J.
Huang, Q.
Yan, S. 
Jiang, S.
Qin, L.
Keywords: Boosting
covariate shift
detector adaptiveness
object detection
transfer learning
Issue Date: May-2011
Source: Pang, J., Huang, Q., Yan, S., Jiang, S., Qin, L. (2011-05). Transferring boosted detectors towards viewpoint and scene adaptiveness. IEEE Transactions on Image Processing 20 (5) : 1388-1400. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2010.2103951
Abstract: In object detection, disparities in distributions between the training samples and the test ones are often inevitable, resulting in degraded performance for application scenarios. In this paper, we focus on the disparities caused by viewpoint and scene changes and propose an efficient solution to these particular cases by adapting generic detectors, assuming boosting style. A pretrained boosting-style detector encodes a priori knowledge in the form of selected features and weak classifier weighting. Towards adaptiveness, the selected features are shifted to the most discriminative locations and scales to compensate for the possible appearance variations. Moreover, the weighting coefficients are further adapted with covariate boost, which maximally utilizes the related training data to enrich the limited new examples. Extensive experiments validate the proposed adaptation mechanism towards viewpoint and scene adaptiveness and show encouraging improvement on detection accuracy over state-of-the-art methods. © 2006 IEEE.
Source Title: IEEE Transactions on Image Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/57697
ISSN: 10577149
DOI: 10.1109/TIP.2010.2103951
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