Learning Weights for Codebook in Image Classification and Retrieval

Cai, H, Yan, F and Mikolajczyk, K (2010) Learning Weights for Codebook in Image Classification and Retrieval In: IEEE Conference on Computer Vision and Pattern Recognition, 2010-06-13 - 2010-06-18.

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Abstract

This paper presents a codebook learning approach for image classification and retrieval. It corresponds to learning a weighted similarity metric to satisfy that the weighted similarity between the same labeled images is larger than that between the differently labeled images with largest margin. We formulate the learning problem as a convex quadratic programming and adopt alternating optimization to solve it efficiently. Experiments on both synthetic and real datasets validate the approach. The codebook learning improves the performance, in particular in the case where the number of training examples is not sufficient for large size codebook.

Item Type:	Conference or Workshop Item (Conference Paper)
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Name Email ORCID Cai, H Yan, F Mikolajczyk, K
Date :	5 August 2010
DOI :	10.1109/CVPR.2010.5539918
Contributors :	Contribution Name Email ORCID http://www.loc.gov/loc.terms/relators/PBL IEEE,
Additional Information :	© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User :	Symplectic Elements
Date Deposited :	21 Dec 2012 12:01
Last Modified :	31 Oct 2017 14:49
URI:	http://epubs.surrey.ac.uk/id/eprint/733272

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