Abstract
When support vector machine (SVM) classifier is applied to image semantic annotation, it usually encounters the problem of excessive training samples. In this paper, we propose a novel method, which is by combining learning vector quantization (LVQ) technique and SVM classifier, to improve annotation accuracy and speed. Affinity propagation algorithm-based LVQ technique is used to optimize the training set, and a few number of optimized representative feature vectors are used to train SVM. This approach not only meets the small sample size characteristic of SVM, but also greatly accelerates the training and annotating process. Comparative experimental studies confirm the validity of the proposed method.
Similar content being viewed by others
References
Lin S, Yao Y, Guo P (2010) Speed up image annotation based on LVQ technique with affinity propagation algorithm. In: Proceedings of international conference on neural information processing, Sydney, Australia, pp 533–540
Morris Y, Takahashi H, Oka R (1999) Image-to-word transformation based on dividing and vector quantizing images with words. In: Proceedings of first internat workshop on multimedia intelligent storage and retrieval management, Orlando, pp 405–409
Duygulu P, Barnard K, Freitas N, Forsyth D (2002) Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In: Proceedings of European conference on computer vision, Copenhagen, Denmark, pp 97–112
Jeon J, Lavrenko V, Manmatha R (2003) Automatic image annotation and retrieval using cross- media relevance models. In: Proceeding of international ACM SIGIR Conference on research and development in information retrieval, Toronto, Canada, pp 119–126
Lavrenko V, Manmatha R, Jeon J (2003) A model for learning the semantics of pietures. In: Proceeding of advances in neural information processing systems, pp 553–560
Feng S, Manmatha R, Lavrenko V (2004) Multiple Bernoulli relevance models for image and video annotation. In: Proceedings of IEEE international conference on computer vision and pattern recognition, Washington DC, USA, pp 1002–1009
Li J, Wang J (2003) Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans Pattern Anal Mach Intell 25(9):1075–1088
Chang E, Goh K, Sychay G, Wu G (2003) Content-based soft annotation for multimodal image retrieval using Bayes point machines. IEEE Trans Circ Syst Video Technol 13((1):26–38
Carneiro G, Chan AB, Moreno PJ, Vasconcelos N (2007) Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans Pattern Anal Mach Intell 29:394–410
Lin W, Oakes M, Tait J (2010) Improving image annotation via representative feature vector selection. Neurocomputing 73:1774–1782
Vapnik VN (1995) The nature of statistical learning theory. Springer, New York
Kohenen T (2001) Self-Organizing Maps. Springer, Berlin
Jolliffe I (1996) Principal component analysis. Springer, Berlin
Viitaniemi V, Laaksonen J (2007) Evaluating the performance in automatic image annotation: example case by adaptive fusion of global image features. Image Comm 22(6):557–568
Vailaya A, Figueiredo MAT, Jain AK, Zhang HJ (2001) Image classification for content-based indexing. IEEE Trans Image Process 10(1):117–130
Yang H, Lee C (2008) Image semantics discovery from web pages for semantic-based image retrieval using self-organizing maps. Expert Syst Appl 34:266–279
Jiang ZH, He J, Guo P (2010) Feature data optimization with LVQ technique in semantic image annotation. In: Proceedings of ISDA2010, Cairo, Egypt, pp 906–911
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315:972–976
Dueck D, Frey BJ (2007) Non-metric affinity propagation for unsupervised image categorization. In: Proceedings of IEEE international conf. on computer vision, Rio De Janeiro, Brazil, pp 1–8
Yang D, Guo P (2009) Improvement of image modeling with affinity propagation algorithm for image semantic annotation. In: Proceedings of international conference on neural information processing, Bangkok, Thailand, pp 778–787
Yang D, Guo P (2010) Image modeling with combined optimization techniques for image semantic annotation. Neural Comput Appl 1–15
Haralick RM, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans Syst Man Cyb 3(6):610–621
Tamura H, Mori S, Yamawaki T (1978) Texture features corresponding to visual perception. IEEE Trans Syst Man Cyb 8(6):460–473
Stanford vision lab, http://vision.stanford.edu/resources_links.html
Kennedy J, Eberhart RC (1995) Particle Swarm optimization. In: Proceedings of the IEEE international joint conference, Neural Networks, pp 1942–1948
LIBSVM-A, http://www.csie.ntu.edu.tw/∼cjlin/libsvm
VOC2008, http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2008/
Guo P, Jia YD, Lyu MR (2008) A study of regularized Gaussian classifier in high-dimension small sample set case based on MDL principle with application to spectrum recognition. Pattern Recognit 41(9):2842–2854
Acknowledgments
The research work described in this paper was fully supported by the grants from the National Natural Science Foundation of China (Project No. 90820010, 60911130513)
Author information
Authors and Affiliations
Corresponding author
Additional information
This work is an extended version of the paper presented at the 2010 International Conference on Neural Information Processing (ICONIP) [1].
Rights and permissions
About this article
Cite this article
Guo, P., Jiang, Z., Lin, S. et al. Combining LVQ with SVM technique for image semantic annotation. Neural Comput & Applic 21, 735–746 (2012). https://doi.org/10.1007/s00521-011-0651-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-011-0651-1