Abstract
Dissimilarity measurement plays a crucial role in content-based image retrieval, where data objects and queries are represented as vectors in high-dimensional content feature spaces. Given the large number of dissimilarity measures that exist in many fields, a crucial research question arises: Is there a dependency, if yes, what is the dependency, of a dissimilarity measure’s retrieval performance, on different feature spaces? In this paper, we summarize fourteen core dissimilarity measures and classify them into three categories. A systematic performance comparison is carried out to test the effectiveness of these dissimilarity measures with six different feature spaces and some of their combinations on the Corel image collection. From our experimental results, we have drawn a number of observations and insights on dissimilarity measurement in content-based image retrieval, which will lay a foundation for developing more effective image search technologies.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Chen, C.-C., Chu, H.-T.: Similarity measurement between images. In: Proceedings of the 29th Annual International Computer Software and Applications Conference (COMPSAC 2005), IEEE, Los Alamitos (2005)
Geman, D., Geman, S., Graffigne, C., Dong, P.: Boundary Detection by Constrained Optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7), 609–628 (1990)
Howarth, P., Rüger, S.: Fractional distance measures for content-based image retrieval. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, Springer, Heidelberg (2005)
Kokare, M., Chatterji, B., Biswas, P.: Comparison of similarity metrics for texture image retrieval. In: Proceeding of IEEE Conf. on Convergent Technologies for Asia-Pacific Region, vol. 2, pp. 571–575 (2003)
Luke, B.T.: Pearson’s correlation coefficient. Online (1995)
Noreault, T., McGill, M., Koll, M.B.: A performance evaluation of similarity measures, document term weighting schemes and representations in a Boolean environment. In: Proceeding of the 3rd annual ACM Conference on Research and development in inforamtion retreval, SIGIR 1980, Kent, UK, pp. 57–76. ACM, Butterworth Co. (1980)
Ojala, T., Pietikainen, M., Harwood, D.: Comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29(1), 51–59 (1996)
Pickering, M.J., Rüger, S.: Evaluation of key frame-based retrieval techniques for video. Computer Vision and Image Understanding 92(2-3), 217–235 (2003)
Puzicha, J.: Distribution-Based Image Similarity, ch. 7, pp. 143–164. Kluwer Academic Publishers, Dordrecht (2001)
Puzicha, J., Hofmann, T., Buhmann, J.M.: Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, San Juan (1997)
Puzicha, J., Rubner, Y., Tomasi, C., Buhmann, J.M.: Empirical evaluation of dissimilarity measures for color and texture. In: Proceeding of the international conference on computer vision, vol. 2, pp. 1165–1172 (September 1999)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. International Journal of Computer Vision 40(2), 99–121 (2004)
Zhang, D., Lu, G.: Evaluation of similarity measurement for image retrieval. In: Procedding of IEEE International Conference on Neural Networks Signal, Nanjing, December 2003, pp. 928–931. IEEE, Los Alamitos (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, H., Song, D., Rüger, S., Hu, R., Uren, V. (2008). Comparing Dissimilarity Measures for Content-Based Image Retrieval. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-68636-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68633-0
Online ISBN: 978-3-540-68636-1
eBook Packages: Computer ScienceComputer Science (R0)