Abstract
This paper describes a concept of image retrieval method based on graph theory, used to speed up the process of edge detection and to represent results in more efficient way. We assume that result representation of edge detection based on graph theory is more efficient than standard map-based representation. Advantages of graph-based representation are direct access to edge nodes of the shape without search and segmentation of edges points as is the case with map-based representations. Another advance is less data consumption, only data for nodes and their connections are needed, what is important in large database applications for good scalability.
In the described approach we reduce the amount of necessary image data to examine by modifying some standard edge detection method. To obtain that, we use an auxiliary grid to detect points of edge intersections with grid lines. Each intersection point becomes a node of graph that is a base element of the graph-based representation. Finally, our method based on edge segmentation creates connections between graph nodes determined in the previous steps of the algorithm. The method analyzes an image independently in squares determined by an auxiliary grid, which can be fork and parallel processed. We motivate the idea of our work that it will be used to develop a method for image feature extraction in CBIR for database applications.
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
Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986)
Holtzman-Gazit, M., Lihi, Z.-M., Irad, Y.: Salient Edges: A Multi Scale Approach. In: ECCV 2010 Workshop on Vision for Cognitive Tasks (2010)
Janakiraman, T.N., Mouli, P.V.S.: Image Segmentation Based on Minimal Spanning Tree and Cycles. In: International Conference on Computational Intelligence and Multimedia Applications, December 13-15, vol. 3, pp. 215–219 (2007)
Karande, K.J.: Multiscale wavelet based edge detection and Independent Component Analysis (ICA) for Face Recognition. In: 2012 International Conference on Communication, Information & Computing Technology, ICCICT. IEEE (2012)
Ogiela, M.R., Tadeusiewicz, R., Ogiela, L.: Intelligent Semantic Information Retrieval in Medical Pattern Cognitive Analysis. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3483, pp. 852–857. Springer, Heidelberg (2005)
Scherer, R., Rutkowski, L.: Neuro-fuzzy relational classifiers. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 376–380. Springer, Heidelberg (2004)
Scherer, R., Rutkowski, L.: A fuzzy relational system with linguistic antecedent certainty factors. In: 6th International Conference on Neural Networks and Soft Computing, Zakopane, Poland, June 11-15. Advances in Soft Computing, pp. 563–569 (2002)
Scherer, R., Rutkowski, L.: Connectionist fuzzy relational systems. In: Halgamuge, S.K., Wang, L. (eds.) Computational Intelligence for Modelling and Prediction. SCI, vol. 2, pp. 35–47. Springer, Heidelberg (2002)
Senthilkumaran, N., Rajesh, R.: Edge detection techniques for image segmentation-A survey of soft computing approaches. International Journal of Recent Trends in Engineering 1(2), 250–254 (2009)
Sotak, G.E., Boyer, K.L.: The Laplacian-of-Gaussian kernel: a formal analysis and design procedure for fast, accurate convolution and full-frame output. Computer Vision, Graphics, and Image Processing 48(2), 147–189 (1989)
Tadeusiewicz, R., Ogiela, L., Ogiela, M.R.: The Automatic Understanding Approach to Systems Analysis and Design. International Journal of Information Management 28(1), 38–48 (2008)
Tadeusiewicz, R., Ogiela, L., Ogiela, M.R.: Cognitive Analysis Techniques in Business Planning and Decision Support Systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 1027–1039. Springer, Heidelberg (2006)
Wu, Z., Leahy, R.: Image segmentation via edge contour finding: a graph theoretic approach. In: Proceedings of the 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1992, June 15-18, pp. 613–619 (1992)
Wei, H., et al.: A novel content-adaptive image compression system. In: 2012 IEEE Visual Communications and Image Processing, VCIP. IEEE (2012)
Xu, L., et al.: The rapid method for road extraction from high-resolution satellite images based on USM algorithm. In: 2012 International Conference on Image Analysis and Signal Processing, IASP. IEEE (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Najgebauer, P., Nowak, T., Romanowski, J., Rygał, J., Korytkowski, M. (2013). Representation of Edge Detection Results Based on Graph Theory. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-38658-9_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38657-2
Online ISBN: 978-3-642-38658-9
eBook Packages: Computer ScienceComputer Science (R0)