By transferring of prior knowledge from source domains and synthesizing the new knowledge extracted from the target domain, the performance of learning can be improved when there are insufficient training data in the target domain. In this paper we propose a new method to transfer a deformable part model (DPM) for object detection, using sharable filters from offline-trained auxiliary DPMs of similar categories and new filters learnt from the target training samples to improve the performance of the target object detector. A DPM consists of a collection of root and part filters. The filters of the auxiliary detectors capture the sharable appearance features and can be used as prior knowledge. The sharable filters are employed by the new detector with a coefficient reweighting algorithm to fit the target object much better. Meanwhile the target object still has some distinct local appearance features that the part filters in the auxiliary filter pool can not represent. Hence, new part filters will be learnt with the training samples of the target object and added to the filter pool as complementary. The final learnt model will be an assembly of transferred auxiliary filters and additional target filters. With a latent transfer learning algorithm, appropriate local features are extracted for the transfer of the auxiliary filters and the description of the distinct target filters. Our experiments demonstrate that the proposed strategy precedes some state-of-the-art methods.

Date Published: 19 December 2013
PDF: 6 pages
Proc. SPIE 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 90450I (19 December 2013); doi: 10.1117/12.2036686

Published in SPIE Proceedings Vol. 9045:
2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
Xinggang Lin; Jesse Zheng, Editor(s)