DocumentCode :
2604157
Title :
Randomness and sparsity induced codebook learning with application to cancer image classification
Author :
Li, Quannan ; Yao, Cong ; Wang, Liwei ; Tu, Zhuowen
fYear :
2012
fDate :
16-21 June 2012
Firstpage :
16
Lastpage :
23
Abstract :
Codebook learning is one of the central research topics in computer vision and machine learning. In this paper, we propose a new codebook learning algorithm, Randomized Forest Sparse Coding (RFSC), by harvesting the following three concepts: (1) ensemble learning, (2) divide-and-conquer, and (3) sparse coding. Given a set of training data, a randomized tree can be used to perform data partition (divide-and-conquer); after a tree is built, a number of bases are learned from the data within each leaf node for a sparse representation (subspace learning via sparse coding); multiple trees with diversities are trained (ensemble), and the collection of bases of these trees constitute the codebook. These three concepts in our codebook learning algorithm have the same target but with different emphasis: subspace learning via sparse coding makes a compact representation, and reduces the information loss; the divide-and-conquer process efficiently obtains the local data clusters; an ensemble of diverse trees provides additional robustness. We have conducted classification experiments on cancer images as well as a variety of natural image datasets and the experiment results demonstrate the efficiency and effectiveness of the proposed method.
Keywords :
cancer; divide and conquer methods; image classification; image coding; learning (artificial intelligence); medical image processing; trees (mathematics); RFSC; cancer image classification; computer vision; diverse trees; divide-and-conquer; ensemble learning; machine learning; randomized forest sparse coding; randomized tree; sparse representation; sparsity induced codebook learning algorithm; subspace learning; Cancer; Encoding; Entropy; Machine learning; Manifolds; Optimization; Vegetation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on
Conference_Location :
Providence, RI
ISSN :
2160-7508
Print_ISBN :
978-1-4673-1611-8
Electronic_ISBN :
2160-7508
Type :
conf
DOI :
10.1109/CVPRW.2012.6239242
Filename :
6239242
Link To Document :
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