Image annotation using multi-correlation probabilistic matrix factorization

Authors:
Zechao Li;Jing Liu;Xiaobin Zhu;Tinglin Liu;Hanqing Lu
Affiliations:
Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the international conference on Multimedia
Year:
2010

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Abstract

The image-word correlation estimation is an essential issue in image annotation. In this paper, we propose a multi-correlation probabilistic matrix factorization (MPMF) algorithm for the correlation estimation. Different from the traditional solutions which treat the image-word correlation, image similarity and word relation independently or sequentially, in the proposed MPMF, these three elements are integrated together simultaneously and seamlessly. Specifically, we have derived two low-dimensional sets by conducting a joint factorization upon the word-to-image relation matrix, the image similarity matrix, and the word relation matrix to derive two low-dimensional sets of latent word factors and latent image factors. Finally, the annotation words of each untagged or noisily tagged image can be predicted by reconstructing the image-word correlations with the both derived latent factors. Experimental results on the Corel dataset and a Flickr image dataset show the superior performance of our proposed algorithm over the state-of-the-arts.