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Similarity learning for object recognition based on derived kernel
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DOI：10.1016/j.neucom.2011.12.005.png)
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Abstract
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Recently, derived kernel method which is a hierarchical learning method and leads to an effective similarity measure has been proposed by Smale. It can be used in a variety of application domains such as object recognition, text categorization and classification of genomic data. The templates involved in the construction of the derived kernel play an important role. To learn more effective similarity measure, a new template selection method is proposed in this paper. In this method, the redundancy is reduced and the label information of the training images is used. In this way, the proposed method can obtain compact template sets with better discrimination ability. Experiments on four standard databases show that the derived kernel based on the proposed method achieves high accuracy with low computational complexity. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Derived kernel
Hierarchical learning
Image similarity
Neural response
Object recognition
Template selection
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