Please use this identifier to cite or link to this item:
|Title:||Hierarchical matching with side information for image classification||Authors:||Chen, Q.
|Issue Date:||2012||Citation:||Chen, Q.,Song, Z.,Hua, Y.,Huang, Z.,Yan, S. (2012). Hierarchical matching with side information for image classification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 3426-3433. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2012.6248083||Abstract:||In this work, we introduce a hierarchical matching framework with so-called side information for image classification based on bag-of-words representation. Each image is expressed as a bag of orderless pairs, each of which includes a local feature vector encoded over a visual dictionary, and its corresponding side information from priors or contexts. The side information is used for hierarchical clustering of the encoded local features. Then a hierarchical matching kernel is derived as the weighted sum of the similarities over the encoded features pooled within clusters at different levels. Finally the new kernel is integrated with popular machine learning algorithms for classification purpose. This framework is quite general and flexible, other practical and powerful algorithms can be easily designed by using this framework as a template and utilizing particular side information for hierarchical clustering of the encoded local features. To tackle the latent spatial mismatch issues in SPM, we design in this work two exemplar algorithms based on two types of side information: object confidence map and visual saliency map, from object detection priors and within-image contexts respectively. The extensive experiments over the Caltech-UCSD Birds 200, Oxford Flowers 17 and 102, PASCAL VOC 2007, and PASCAL VOC 2010 databases show the state-of-the-art performances from these two exemplar algorithms. © 2012 IEEE.||Source Title:||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition||URI:||http://scholarbank.nus.edu.sg/handle/10635/70465||ISBN:||9781467312264||ISSN:||10636919||DOI:||10.1109/CVPR.2012.6248083|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 23, 2019
checked on May 23, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.