Abstract
There are various kinds’ methods to method that image retrieval based on shape feature. In this paper, an efficient content-based image information retrieval method which utilizes shape information and color information is proposed. CSS(Curvature Scale Space) space is used to extract shape information. HSI(Hue Saturation Intensity) space is used to extract color information. This method expresses contours of the object which is binarized through pre-processes in CSS space, then extract shape features of the object in this space. CSS space is a space that expresses curvatures of contours in multiple resolutions, which offers shape features invariant to shift, scale, and skew of the object. HSI color space offers hue and saturation information which is less affected by change of brightness of image. This method gets histogram of the object’s color, and then applies histogram intersection degree to the matching metric in searching process. Show result that image object retrieval being based on process and this that draw CSS information from reflex because an experiment uses ICSS method, and estimate performance by comparing old method and method that propose. The results of experiments show that the proposed method is better in accuracy of searching than existing methods.
This is a preview of subscription content, log in via an institution to check access.
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
Abbasi, S.: Curvature scale space in shape similarity retrieval, Ph.D. thesis, Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 5XH, England (1998)
Mokhtarian, F., Mackworth, A.: Scale-based description and recognition of planar curves and two-dimensional shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(1), 34–43 (1986)
Abbasi, S., Mokhtarian, F.: Robustness of Shape Similarity Retrieval under Affine Transformation (1999)
Bebis, G., Papadourakis, G., Orphanoudakis, S.: Curvature scale space driven object recognition with an indexing scheme based on artificial neural networks accepted Pattern Recognition also available from, http://www.cs.unr.edu/~bebis
Swain, M., Ballard, D.: Color Indexing. Intl’l Journal of Computer Vision 7(1), 11–32 (1991)
Luong, Q.T.: Color in Computer vision. handbook of Pattern Recognition and Computer Vision, 311–368 (1993)
Gudivada, V.N., Raghavan, V.V.: Content-based image retrieval systems. IEEE Computer, pp.18–22 (September 1995)
Stricker, M., Dimai, A.: Color Indexing with Weak Spatial Constraints, Storage and Retrieval for Image and Video Databases IV. In: SPIE Proceedings, vol. 2670 (1996)
Pass, G., Zabih, R.: Histogram refinement for content-based image retrieval. In: IEEE Workshop on Applications of Computer Vision, pp. 96–102. IEEE, Los Alamitos (1996)
Mandal, M.K., Aboulnasr, T., Panchanathan, S.: Image /indexing Using Moments and Wavelets. IEEE Transactions on Consumer Electronics 42(3), 557–565 (1996)
Huang, J., Ravi Kumar, S., Mitra, m., Zhu, W.-J., Zabih, R.: Image Indexing Using Color Correlogram. In: International Conference on Computer Vision and Pattern Recognition, IEEE, Los Alamitos (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ha, J., Choi, H. (2007). The Image Retrieval Method Using Multiple Features. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4705. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74472-6_80
Download citation
DOI: https://doi.org/10.1007/978-3-540-74472-6_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74468-9
Online ISBN: 978-3-540-74472-6
eBook Packages: Computer ScienceComputer Science (R0)