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.
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Nakayama, H., Harada, T., Kuniyoshi, Y., Otsu, N. (2008). High-Performance Image Annotation and Retrieval for Weakly Labeled Images Using Latent Space Learning. In: Huang, YM.R., et al. Advances in Multimedia Information Processing - PCM 2008. PCM 2008. Lecture Notes in Computer Science, vol 5353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89796-5_62
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DOI: https://doi.org/10.1007/978-3-540-89796-5_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89795-8
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