Paper Info
Title
Combining multiple kernels for efficient image classification
Abstract
We investigate the problem of combining multiple feature channels for the purpose of efficient image classification. Discriminative kernel based methods, such as SVMs, have been shown to be quite effective for image classification. To use these methods with several feature channels, one needs to combine base kernels computed from them. Multiple kernel learning is an effective method for combining the base kernels. However, the cost of computing the kernel similarities of a test image with each of the support vectors for all feature channels is extremely high. We propose an alternate method, where training data instances are selected, using AdaBoost, for each of the base kernels. A composite decision function, which can be evaluated by computing kernel similarities with respect to only these chosen instances, is learnt. This method significantly reduces the number of kernel computations required during testing. Experimental results on the benchmark UCI datasets, as well as on a challenging painting dataset, are included to demonstrate the effectiveness of our method.
Year
DOI
Venue
2009
10.1109/WACV.2009.5403040
Applications of Computer Vision
Keywords
Field
DocType
image classification,learning (artificial intelligence),support vector machines,AdaBoost,base kernels,composite decision function,discriminative kernel,image classification,kernel similarity,multiple feature channels,multiple kernel learning,support vector machine
Graph kernel,Radial basis function kernel,Pattern recognition,Kernel embedding of distributions,Computer science,Multiple kernel learning,Support vector machine,Feature extraction,Tree kernel,Artificial intelligence,Machine learning,Standard test image
Conference
ISSN
ISBN
Citations 
1550-5790
978-1-4244-5497-6
12
PageRank 
References 
Authors
0.54
23
3
Name
Order
Citations
PageRank
Behjat Siddiquie1120.54
Shiv Naga Prasad Vitaladevuni227218.18
Larry S. Davis3142012690.83