In this paper we propose to integrate the recently introduces ORB descriptors in the currently favored approach for image classification, that is the Bag of Words model. In particular the problem to be solved is to provide a clustering method able to deal with the binary string nature of the ORB descriptors. We suggest to use a k-means like approach, called k-majority, substituting Euclidean distance with Hamming distance and majority selected vector as the new cluster center. Results combining this new approach with other features are provided over the ImageCLEF 2011 dataset.
A Fast Approach for Integrating ORB Descriptors in the Bag of Words Model / Grana, C., Borghesani, D., Manfredi, M., Cucchiara, R.. - STAMPA. - 8667:(2013), pp. 09-1-09-8. (Multimedia Content and Mobile Devices Burlingame, California, USA Feb 4-6) [10.1117/12.2008460].
A Fast Approach for Integrating ORB Descriptors in the Bag of Words Model
GRANA, Costantino;BORGHESANI, Daniele;MANFREDI, MARCO;CUCCHIARA, Rita
2013
Abstract
In this paper we propose to integrate the recently introduces ORB descriptors in the currently favored approach for image classification, that is the Bag of Words model. In particular the problem to be solved is to provide a clustering method able to deal with the binary string nature of the ORB descriptors. We suggest to use a k-means like approach, called k-majority, substituting Euclidean distance with Hamming distance and majority selected vector as the new cluster center. Results combining this new approach with other features are provided over the ImageCLEF 2011 dataset.
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