Multimodal learning for multi-label image classification

Pang, Y. and Ma, Z. and Yuan, Y. and Li, Xuelong and Wang, K. (2011) Multimodal learning for multi-label image classification. In: UNSPECIFIED (ed.) IEEE International Conference on Image Processing. Institute of Electrical and Electronics Engineers, pp. 1797-1800. ISBN 9781457713040.

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Official URL: http://dx.doi.org/10.1109/ICIP.2011.6115811

Abstract

We tackle the challenge of web image classification using additional tags information. Unlike traditional methods that only use the combination of several low-level features, we try to use semantic concepts to represent images and corresponding tags. At first, we extract the latent topic information by probabilistic latent semantic analysis (pLSA) algorithm, and then use multi-label multiple kernel learning to combine visual and textual features to make a better image classification. In our experiments on PASCAL VOC'07 set and MIR Flickr set, we demonstrate the benefit of using multimodal feature to improve image classification. Specifically, we discover that on the issue of image classification, utilizing latent semantic feature to represent images and associated tags can obtain better classification results than other ways that integrating several low-level features.

Metadata

Item Type:	Book Section
School:	Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
Depositing User:	Sarah Hall
Date Deposited:	07 Jun 2013 10:19
Last Modified:	09 Aug 2023 12:33
URI:	https://eprints.bbk.ac.uk/id/eprint/7378

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