Learning models for object detection is a challenging problem due to the large intra-class variability of objects in appearance, viewpoints, and rigidity. We address this variability by a novel feature pooling method that is adaptive to segmented regions. The proposed detection algorithm automatically discovers a diverse set of exemplars and their distinctive parts which are used to encode the region structure by the proposed feature pooling method. Based on each exemplar and its parts, a regression model is learned with samples selected by a coarse region matching scheme. The proposed algorithm performs favorably on the PASCAL VOC 2007 dataset against existing algorithms. We demonstrate the benefits of our feature pooling method when compared to conventional spatial pyramid pooling features. We also show that object information can be transferred through exemplars for detected objects.
|Title of host publication||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015|
|Publisher||IEEE Computer Society|
|Number of pages||9|
|Publication status||Published - 2015 Oct 14|
|Event||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 - Boston, United States|
Duration: 2015 Jun 7 → 2015 Jun 12
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Other||IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015|
|Period||15/6/7 → 15/6/12|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition