Open Access Open Access  Restricted Access Subscription or Fee Access

An Enhanced Algorithm for Mining Color Images – A Novel Approach

C. Lakshmi Devasena, R. Radha Krishnan, Dr.M. Hemalatha


Image mining is not mere an extension of data mining to image domain. Image mining is a technique normally used to extract knowledge and recognize objects directly from images. Image segmentation will normally be the first step in image mining. Image segmentation is difficult, but it is important problem in computer vision and machine perception. We can treat image segmentation as graph partitioning problem. The minimum spanning tree algorithm is capable of detecting clusters with irregular boundaries to mine images. This paper proposes the minimum spanning tree based clustering algorithm to detect color images using weighted Euclidean distance for edges, which is key element in building the graph from image. The algorithm produces n clusters with segments. An important characteristic of this method is its capacity to conserve information in low variability image regions while omitting detail in high-variability regions. The proposed algorithm has been employed using MATLAB. The implemented system produces promising results.


Clustering, Color Images, Graph Partitioning, Image Mining, Image Segmentation, Weighted Euclidean Minimum Spanning Tree.

Full Text:



Ji Zhang, Wynne Hsu, Mong Li Lee. An Information-driven Framework for Image Mining, in Proceedings of 12th International Conference on Database and Expert Systems Applications (DEXA), Munich, Germany, 2001.

Y. Rui, T. Huang and S. Chang. Image retrieval: current techniques, promising directions and open issues. Journal of Visual Communication and Image Representation, 10(4): 39-62, April 1999.

W. Niblack, R. Barber, and et al. The QBIC project: Querying images by content using color, texture and shape. In Proc. SPIE Storage and Retrieval for Image and Video Databases, Feb 1994.

[James Dowe. Content based retrieval in multimedia imaging. In Proc. SPIE storage and Retrieval for Image and Video Databases, 1993.

A. Pentland, R.W. Picard, and S. Slaroff. Photobook: Content based manipulation of databases. Int. J. Comput. Vis., 18(3): 233-254, 1996.

Avis,D. “Diameter partitioning”. Discrete and Computational Geometry, 1:265-276, 1986.

Johnson,D, “The np-completeness column”: An ongoing guide. Journal of Algorithms,3:182-195, 1982.

Asano,T., Bhattacharya,B., Keil,M. and Yao,F. “Clustering Algorithms based on minimum and maximum spanning trees”. In Proceedings of the 4th Annual Symposium on Computational Geometry, Pages 252-257, 1988.

Zahn,C. “Graph-theoretical methods for detecting and describing gestalt clusters”. IEEE Transactions on Computers, C-20:68-86, 1971.

Paivinen,N. “Clustering with a minimum spanning of scale-free-like structure”. Pattern Recogn. Lett.,26(7):921-930, 2005.

Xu,Y., Olman,V. and Xu,D, “Minimum spanning trees for gene expression data clustering”. Genome Informatics, 12:24-33, 2001.

Felzenszwalb P.F and Huttenloche, “Efficient Graph-Based Image Segmentation”, International Journal of Computer Vision, Volume 59, 2004.

Ming Zhang, Reda Alhajj, “Improving the Graph Based Image Segmentation Method”, Proceedings of the 18 th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’06), IEEE 2006.

Thiadmer Riemersma, “Color Metric” Available at http:// cmetric. Htm.

Deepthi Narayan, Srikanta Murthy K., and Hemantha Kumar G. “Image Segmentation Based on Graph Theoretical Approach to Improve the Quality of Image Segmentation”, World Academy of Science, Engineering and Technology, Volume 42, 2008.

Oleksandr Grygorash, Yan Zhou, Zach Jorgensen. “Minimum spanning Tree Based Clustering Algorithms”. Proceedings of the 18th IEEE International conference on tools with Artificial Intelligence(ICTAI’06), 2006.

[S.John Peter, S.P.Victor “A Novel Algorithm for Central Cluster Using Minimum Spanning Tree” Journal of Theoretical and Applied Information Technology 2005 - 2010 JATIT.

K. Rajendra Prasad, Dr. P.Govinda Rajulu “A Survey on Clustering Technique for datasets using Efficient Graph Structures” International Journal of Engineering Science and Technology Vol. 2 (7), 2010, pp: 2707-2714

S.John Peter, S.P.Victor “A Novel Algorithm for Meta Similarity Clusters Using Minimum Spanning Tree”, IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.2, February 2010, 2010.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.