High-Performance Image Annotation and Retrieval for Weakly Labeled Images Using Latent Space Learning

Authors:
Hideki Nakayama;Tatsuya Harada;Yasuo Kuniyoshi;Nobuyuki Otsu
Affiliations:
Grad. School of Information Science and Technology, Dept. of Mechano-Informatics, The University of Tokyo, Tokyo, Japan 113-8656;Grad. School of Information Science and Technology, Dept. of Mechano-Informatics, The University of Tokyo, Tokyo, Japan 113-8656;Grad. School of Information Science and Technology, Dept. of Mechano-Informatics, The University of Tokyo, Tokyo, Japan 113-8656;National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan 305-8568
Venue:
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2008

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Abstract

Image annotation and retrieval are among the most promising new internet search technologies and have widespread applications. However, the task is very difficult because of the generic nature of the target images. In this paper, we propose a high speed and high accuracy image annotation and retrieval method for miscellaneous objects and scenes. This method combines the higher-order local auto-correlation (HLAC) features with the probabilistic canonical correlation analysis framework. A distance between images can be defined in the intrinsic feature space for annotation using latent space learning between images and labels. The HLAC features have additive and position invariance properties, which makes them well-suited for images in which the positions and number of objects are arbitrary. The proposed method is shown to be faster and more accurate than previously published methods.