Assemble new object detector with transferred auxiliary filters and distinct target filters

摘要：

　　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.

关键词： transfer learning deformable part model object detection

作者: Zhiwei Ruan Chenbo Shi Xinggang Lin

作者单位: Department of Electronic Engineering,Tsinghua University,Beijing 100084,China

会议类型: 国际会议

会议名称: 2013 International Conference on Optical Instrument and Technology (OIT’2013)(2013年光学仪器与技术国际会议)

会议地点: 北京

会议语种:英文

页码: 1-6

在线出版日期: 2013-11-17（万方平台首次上网日期，不代表论文的发表时间）