Abstract
This paper addresses the challenge of automatic annotation of images for semantic image retrieval. In this research, we aim to identify visual features that are suitable for semantic annotation tasks. We propose an image classification system that combines MPEG-7 visual descriptors and support vector machines. The system is applied to annotate cityscape and landscape images. For this task, our analysis shows that the colour structure and edge histogram descriptors perform best, compared to a wide range of MPEG-7 visual descriptors. On a dataset of 7200 landscape and cityscape images representing real-life varied quality and resolution, the MPEG-7 colour structure descriptor and edge histogram descriptor achieve a classification rate of 82.8% and 84.6%, respectively. By combining these two features, we are able to achieve a classification rate of 89.7%. Our results demonstrate that combining salient features can significantly improve classification of images.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Long, F., Zhang, H., Feng, D.: Fundamentals of content-based image retrieval. In: Feng, D., Siu, W.C., Zhang, H.J. (eds.) Multimedia Information Retrieval and Management - Technological Fundamentals and Applications, Springer, Heidelberg (2002)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Datta, R., Li, J., Wang, J.Z.: Content-based image retrieval: approaches and trends of the new age. In: The 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 253–262. ACM Press, New York (2005)
Eakins, J.P.: Retrieval of still images by content. In: Agosti, M., Crestani, F., Pasi, G. (eds.) ESSIR 2000. LNCS, vol. 1980, pp. 111–138. Springer, Heidelberg (2001)
Yiu, E.C.: Image classification using color cues and texture orientation. PhD thesis, Massachusetts Institute of Technology (1996)
Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: IEEE International Workshop on Content-Based Access of Image and Video Database, pp. 42–51. IEEE Computer Society Press, Los Alamitos (1998)
Vailaya, A., Jain, A., Zhang, H.J.: On image classification: city vs. landscape. In: IEEE Workshop on Content-Based Access of Image and Video Libraries, pp. 3–8. IEEE Computer Society Press, Los Alamitos (1998)
Vailaya, A., Figueiredo, M., Jain, A., Zhang, H.J.: Content-based hierarchical classification of vacation images. IEEE International Conference on Multimedia Computing and Systems 1, 518–523 (1999)
Lienhart, R., Hartmann, A.: Classifying images on the web automatically. Journal of Electronic Imaging 11(4), 445–454 (2002)
Hu, G.H., Bu, J.J., Chen, C.: A novel bayesian framework for indoor-outdoor image classification. International Conference on Machine Learning and Cybernetics 5, 3028–3032 (2003)
Gonzalez, R.C., Woods, R.E.: Digital image processing. Prentice Hall, New York (2002)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital image processing using MATLAB. Prentice Hall, New York (2004)
Manjunath, B.S., Salembier, P., Sikora, T. (eds.): Introduction to MPEG-7: Multimedia content description interface. Wiley, Milton (2002)
MPEG-7 Video Group. Text of ISO/IEC 15938-3/FDIS information technology - Multimedia Content Description Interface - Part 3 Visual. In: ISO/IEC JTC1/SC29/WG11/N4358, Sydney (2001)
Nack, F., Lindsay, A.T.: Everything you wanted to know about MPEG-7, part 1. IEEE Multimedia 6(3), 65–77 (1999)
Nack, F., Lindsay, A.T.: Everything you wanted to know about MPEG-7part 2. IEEE Multimedia 6(4), 64–73 (1999)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2001)
Abe, S.: Support vector machines for pattern classification. Springer, New York (2005)
Institute for Integrated Systems. MPEG-7 eXperimentation Model (XM), Software (2005), available at http://www.lis.e-technik.tu-muenchen.de/research/bv/topics/mmdb/mpeg7.html
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines, Software (2007), available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shao, W., Naghdy, G., Phung, S.L. (2007). Automatic Image Annotation for Semantic Image Retrieval. In: Qiu, G., Leung, C., Xue, X., Laurini, R. (eds) Advances in Visual Information Systems. VISUAL 2007. Lecture Notes in Computer Science, vol 4781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76414-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-76414-4_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76413-7
Online ISBN: 978-3-540-76414-4
eBook Packages: Computer ScienceComputer Science (R0)