[CODE] PASCAL Challenge Winning Prize, 2011
(2012-11-08 09:36:41)
Image Classification: An Integration of Randomization and Discrimination
in A Dense Feature Representation
Introduction
The goal of our method is to identify the discriminative fine-grained image region
that distinguishes different classes. To achieve this goal we sample image regions
from dense sampling space and use a random forest algorithm with discriminative
classifier. Each node of the tree of random forest is trained and tested with fine-
grained image patches combining the information from upstream nodes together. We
implemented each node of the tree with a discriminative SVM classifier, which makes
the node as a strong classifier.
PASCAL VOC Winner Prize
Our method achieves the best performance in 6 out of the 10 classes in the
PASCAL VOC action classification challenge. The table below shows the average precision of our
method for each action category.
Source Code
You can download the code of the project
here. For instruction how to use the code,
please open README file after extracting the files.
References
B. Yao, A. Khosla, and L. Fei-Fei. "Combining Randomization and Discrimination for
Fine-Grained Image Categorization."IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). Colorado Springs, CO, USA. June 21-25, 2011.
[PDF][Slides] [BibTeX]Contact
Please contact bangpeng@cs.stanford.edu if you have any question.
Link：
http://ai.stanford.edu/~bangpeng/discrim_rf.html
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