Image annotation and retrieval based on efficient learning of contextual latent space

Authors:
Tatsuya Harada;Hideki Nakayama;Yasuo Kuniyoshi
Affiliations:
Dept. of Mechano-Informatics, Grad. School of Information Science and Technology, The University of Tokyo, Tokyo, Japan;Dept. of Mechano-Informatics, Grad. School of Information Science and Technology, The University of Tokyo, Tokyo, Japan;Dept. of Mechano-Informatics, Grad. School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

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Abstract

Image annotation and retrieval are extremely difficult because of the generic nature of the target images. Generic images contain various miscellaneous objects and scenes. Therefore, desirable annotation results are subjective and underspecified. To overcome this problem, it is important to assume "Weak Labeling" framework, where images are weakly related to multiple words without region information. In this paper, we propose a high speed and high accuracy image annotation and retrieval method based on efficient learning of the contextual latent space. A distance between samples can be defined in the intrinsic feature space for annotation using latent space learning between images and labels. The proposed method is shown to be faster and more accurate than previously published methods.