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An Empirical Study of Visual Features for Part Based Model
Junge Zhang; Yinan Yu; Shuai Zheng; Kaiqi Huang; Tieniu Tan
Conference NameThe First Asian Conference on Pattern Recognition
Source PublicationPattern Recognition, 2011
Conference Date2011
Conference PlaceBeijing, China
AbstractObject detection is a fundamental task in computer vision. Deformable part based model has achieved great success in the past several years, demonstrating very promising performance. Many papers emerge on part based model such as structure learning, learning more discriminative features. To help researchers better understand the existing visual features' potential for part based object detection and promote the deep research into part based object representation, we propose an evaluation framework to compare various visual features' performance for part based model. The evaluation is conducted on challenging PASCAL VOC2007 dataset which is widely recognized as a benchmark database. We adopt Average Precision (AP) score to measure each detector's performance. Finally, the full evaluation results are present and discussed.
KeywordComputer Vision   image Representation   object Detection 
Document Type会议论文
Corresponding AuthorKaiqi Huang
Recommended Citation
GB/T 7714
Junge Zhang,Yinan Yu,Shuai Zheng,et al. An Empirical Study of Visual Features for Part Based Model[C],2011:219-223.
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