Title
Combining multi-scale dissimilarities for image classification
Abstract
In image classification, multi-scale information is usually combined by concatenating features or selecting scales. Their main drawbacks are that concatenation increases the feature dimensionality by the number of scales and scale selection typically loses the information from other scales. We propose to solve this problem by the dissimilarity representation as it enables to combine various sources of information without increasing the dimensionality of the representation space. Various combining rules are introduced and tested with real-world applications. Our experiments show that combining with dissimilarities from all scales could indeed improve considerably upon the performance of the best single scale and adaptive combining can improve upon straightforward averaging.
Year
Venue
Keywords
2012
ICPR
representation space dimensionality,image representation,multiscale image classification dissimilarities,image matching,real-world applications,information sources,dissimilarity representation,feature extraction,image classification,feature dimensionality,selecting scales,features scales,multiscale information
Field
DocType
ISSN
Computer vision,Feature detection (computer vision),Pattern recognition,Computer science,Scale space,Curse of dimensionality,Feature extraction,Artificial intelligence,Concatenation,Combining rules,Scale selection,Contextual image classification
Conference
1051-4651
ISBN
Citations 
PageRank 
978-1-4673-2216-4
1
0.35
References 
Authors
9
3
Name
Order
Citations
PageRank
Yan Li1483.34
Robert P. W. Duin24322336.00
Marco Loog31796154.31