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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Duygulu, P., Barnard, K., Freitas, D.F.N.: Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In: Proc. European Conf. Computer Vision, pp. 349–354 (2002)
Jeon, J., Lavrenko, V., Manmatha, R.: Automatic image annotation and retrieval using crossmedia relevance models. In: Proc. ACM SIGIR Conf., pp. 119–126 (2003)
Lavrenko, V., Manmatha, R., Jeon, J.: A model for learning the semantics of pictures. In: Advances in Neural Information Processing Systems (2003)
Mori, Y., Takahashi, H., Oka, R.: Image-to-word transformation based on dividing and vector quantizing images with words. In: MISRM 1999 First International Workshop on Multimedia Intelligent Storage and Retrieval Management (1999)
Feng, S., Manmatha, R., Lavrenko, V.: Multiple bernoulli relevance models for image and video annotation. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1002–1009 (2004)
Carneiro, G., Chan, A.B., Moreno, P.J., Vasconcelos, N.: Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence 29(3), 394–410 (2007)
Carneiro, G., Vasconcelos, N.: Formulating semantic image annotation as a supervised learning problem. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 163–168 (2005)
Bach, F.R., Jordan, M.I.: A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley (2005)
Kato, T., Kurita, T., Otsu, N., Hirata, K.: A sketch retrieval method for full color image database –query by visual example–. In: Proc. of 11th International Conference on Pattern Recognition, vol. 2, pp. 213–216 (1992)
Otsu, N., Kurita, T.: A new scheme for practical, flexible and intelligent vision systems. In: Proc. IAPR Workshop on Computer Vision (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-89796-5_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89795-8
Online ISBN: 978-3-540-89796-5
eBook Packages: Computer ScienceComputer Science (R0)