DocumentCode :
3268683
Title :
Region-based image categorization with reduced feature set
Author :
Herman, Gunawan ; Ye, Getian ; Xu, Jie ; Zhang, Bang
Author_Institution :
Making Sense of Data Group, Univ. of New South Wales, Kensington, NSW
fYear :
2008
fDate :
8-10 Oct. 2008
Firstpage :
586
Lastpage :
591
Abstract :
In this paper we propose a new algorithm for region-based image categorization that is formulated as a multiple instance learning (MIL) problem. The proposed algorithm transforms the MIL problem into a traditional supervised learning problem, and solves it using a standard supervised learning method. The features used in the proposed algorithm are the hyperclique patterns which are ldquocondensedrdquo into a small set of discriminative features. Each hyperclique pattern consists of multiple strongly-correlated instances (i.e., features). As a result, hyperclique patterns are able to capture the information that are not shared by individual features. The advantages of the proposed algorithm over existing algorithms are threefold: (i) unlike some existing algorithms which use learning methods that are specifically designed for MIL or for certain datasets, the proposed algorithm uses a general-purpose standard supervised learning method, (ii) it uses a significantly small set of features which are empirically more discriminative than the PCA features (i.e. principal components), and (iii) it is simple and efficient and achieves a comparable performance to most state-of-the-art algorithms. The efficiency and good performance of the proposed algorithm make it a practical solution to general MIL problems. In this paper, we apply the proposed algorithm to both drug activity prediction and image categorization, and promising results are obtained.
Keywords :
image processing; learning (artificial intelligence); principal component analysis; PCA features; feature set reduction; general-purpose standard supervised learning method; hyperclique patterns; multiple instance learning; region-based image categorization; Algorithm design and analysis; Australia; Computer science; Data engineering; Drugs; Humans; Image segmentation; Learning systems; Principal component analysis; Supervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Signal Processing, 2008 IEEE 10th Workshop on
Conference_Location :
Cairns, Qld
Print_ISBN :
978-1-4244-2294-4
Electronic_ISBN :
978-1-4244-2295-1
Type :
conf
DOI :
10.1109/MMSP.2008.4665145
Filename :
4665145
Link To Document :
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