Abstract | ||
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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 |
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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 Li | 1 | 48 | 3.34 |
Robert P. W. Duin | 2 | 4322 | 336.00 |
Marco Loog | 3 | 1796 | 154.31 |