We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Fast and Efficient K-means based Algorithm to Content-based Image Clustering

by Basel Hafiz, Mohamad Mousa, Mohamad Waheed
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 152 - Number 5
Year of Publication: 2016
Authors: Basel Hafiz, Mohamad Mousa, Mohamad Waheed
10.5120/ijca2016910715

Basel Hafiz, Mohamad Mousa, Mohamad Waheed . Fast and Efficient K-means based Algorithm to Content-based Image Clustering. International Journal of Computer Applications. 152, 5 ( Oct 2016), 8-14. DOI=10.5120/ijca2016910715

@article{ 10.5120/ijca2016910715,
author = { Basel Hafiz, Mohamad Mousa, Mohamad Waheed },
title = { Fast and Efficient K-means based Algorithm to Content-based Image Clustering },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 152 },
number = { 5 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume152/number5/26313-2016910715/ },
doi = { 10.5120/ijca2016910715 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:57:20.665527+05:30
%A Basel Hafiz
%A Mohamad Mousa
%A Mohamad Waheed
%T Fast and Efficient K-means based Algorithm to Content-based Image Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 152
%N 5
%P 8-14
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Some of the present approaches compare the user’s query image against all of the database images; as a result, the computational complexity and search space will boost, respectively. The fundamental purpose of the research presented in the present paper is to evolve a general purpose clustering method that can efficiently and effectively handle large capacity image databases. It can be fluently embedded into distinct CBIR systems. In this paper, we developed a novel content-based image clustering technique based on an effective k-means based algorithm. The co-occurrence matrix features and color moments are utilized to evolve an effective and innovative image clustering framework. The texture and color features are integrated to enhancing results obtained using individual descriptors. The introduced k-means based clustering algorithm has been proposed as a preprocessing procedure to accelerate image retrieval and to enhance image retrieval accuracy. The experimental outcomes based on COREL images have been investigated and indicated considerable refinement in terms of quality of image clustering, retrieval accuracy, and speed compared against the conventional k-means method.

References
  1. Datta R., Joshi D., Li J., and Wang J.Z., "Image Retrieval: Ideas, Influences, and Trends of the New Age," ACM Comput Surv, vol. 40, no. 2, pp. 5:1-60, 2008.
  2. Alnihoud J., “Content-based Image Retrieval System Based on Self Organizing Map, Fuzzy Color Histogram and Subtractive Fuzzy Clustering,” The International Arab Journal of Information Technology, vol. 9, no. 5, pp. 452-458, 2012.
  3. Hurtut T., Gousseau Y., and Schmitt F., "Adaptive Image Retrieval Based on the Spatial Organization of Colors," Comput Vis Image Und, vol. 112, pp. 101–113, 2008.
  4. Karthikeyan M. and Aruna P., "Probability Based Document Clustering and Image Clustering using Content-based Image Retrieval," Appl Soft Comput, vol. 13, no. 2, pp. 959–966, 2013.
  5. Lin C. and Lin W., "Image Retrieval System Based on Adaptive Color Histogram and Texture Features," Comput J, vol. 54, no. 7, pp. 1136–1147, 2010.
  6. Lin C., Chan Y., Chen K., Huang D., and Chang Y., "Fast Color Spatial Feature Based Image Retrieval Methods," Expert Syst Appl, vol. 39, no. 9, pp. 11412–11420, 2011.
  7. Lin C., Chen R., and Chan Y., "A Smart Content-based Image Retrieval System Based on Color and Texture Feature," Image Vision Comput, vol. 27, no. 6, pp. 658–665, 2009.
  8. Min R. and Cheng H., "Effective Image Retrieval using Dominant Color Descriptor and Fuzzy Support Vector Machine," Pattern Recognit, vol. 42, no. 1, pp. 147–157, 2009.
  9. Młynarczuk M., Gorszczyk A., and S´lipek B., "The Application of Pattern Recognition in the Automatic Classification of Microscopic Rock Images," Comput Geosci, vol. 60, pp. 126–133, 2013.
  10. Nezamabadi-Pour H. and Kabir E., "Image Retrieval using Histograms of Unicolor and Bi-color Blocks and Directional Changes in Intensity Gradient," Pattern Recognit Lett, vol. 25, no. 14, pp. 1547–1557, 2004.
  11. Serrano-Talamantes J., Aviles-Cruz C., Villegas-Cortez J., and Sossa-Azuela J., "Self Organizing Natural Scene Image Retrieval," Expert Syst Appl, vol. 40, no. 7, pp. 2398–2409, 2013.
  12. Subrahmanyam M., Jonathan Wu Q., Maheshwari R., and Balasubramanian R., "Modified Color Motif Co-occurrence Matrix for Image Indexing and Retrieval," Comput Electr Eng, vol. 39, no. 3, pp. 762–774, 2013.
  13. Subramanian M. and Sathappan S., "An Efficient Content Based Image Retrieval using Advanced Filter Approaches," The International Arab Journal of Information Technology, vol. 12, no. 3, pp. 229-236, 2015.
  14. Tajeripour F., Saberi M., and Fekri-Ershad S., "Developing a Novel Approach for Content Based Image Retrieval Using Modified Local Binary Patterns and Morphological Transform," The International Arab Journal of Information Technology, vol. 12, no. 6, pp. 574-581, 2015.
  15. Wang H., Mohamad D., and Ismail N.-A., "An Efficient Parameters Selection for Object Recognition Based Colour Features in Traffic Image Retrieval," The International Arab Journal of Information Technology, vol. 11, no. 3, pp. 308-314, 2014.
  16. Yildizer E., Metin Balci A., Jarada T., and Alhajj R., "Integrating Wavelets with Clustering and Indexing for Effective Content-based Image Retrieval," Knowl-Based Syst, vol. 31, pp. 55–66, 2012.
  17. Hafiane A. and Zavidovique B., "Local Relational String and Mutual Matching for Image Retrieval," Inform Process Manag, vol. 44, no. 3, pp. 1201–1213, 2008.
  18. Fonseca M.J. and Jorge J.A., "Towards Content-based Retrieval of Technical Drawings Through High-dimensional Indexing," Comput Graph, vol. 27, no. 1, pp. 61–69, 2003.
  19. Raveaux R., Burie J.-C., and Ogier J.-M., "Structured Representations in a Content-based Image Retrieval Context," J Vis Commun Image Represent, vol. 24, no. 8, pp. 1252–1268, 2013.
  20. Frosini P. and Landi C., "Persistent Betti Numbers for a Noise Tolerant Shape-based Approach to Image Retrieval," Pattern Recognit Lett, vol. 34, pp. 863–872, 2013.
  21. Lee S., "Symmetry-driven Shape Description for Image Retrieval," Image Vision Comput, vol. 31, no. 4, pp. 357–363, 2013.
  22. Mohd Anuar F., Setchi R., and Lai Y.-K., "Trademark Image Retrieval using an Integrated Shape Descriptor," Expert Syst Appl, vol. 40, no. 1, pp. 105–121, 2013.
  23. Haridas K. and Thanamani A., "An Efficient Image Clustering and Content-based Image Retrieval using Fuzzy K-means Clustering Algorithm," International Review on Computers and Software (IRECOS), vol. 9, no. 1, pp. 147-153, 2014.
  24. Maheshwari M., Silakari S., and Motwani M., "Image Clustering using Color and Texture," Proc. 1st Conference on Computational Intelligence, Communication Systems, and Networks (CICSYN), pp. 403–408, 2009.
  25. Narasimhan H. and Ramraj P., "Contribution-based Clustering Algorithm for Content-based Image Retrieval," Proc. 5th Conference on Industrial and Information Systems (ICIIS), pp. 442–447, 2010.
  26. Shrivastava R., Upadhyay K., Bhati R., and Mishra D., "Comparison between K-Mean and C-Mean Clustering for CBIR," Proc. 2nd Conference on Computational Intelligence, Modeling, and Simulation (CIMSIM), pp. 117-118, 2010.
  27. Davis R.A., Zhongmiao Xiao, and Xiaojun Qi, "Capturing Semantic Relationship among Images in Clusters for Efficient Content-based Image Retrieval," Proc. 19th IEEE Conference on Image Processing (ICIP), pp. 1953–1956, 2012.
  28. Rasli R., Muda T., Yusof Y., and Bakar J., "Comparative Analysis of Content-based Image Retrieval Techniques using Color Histogram: A Case Study of GLCM and K-Means Clustering," Proc. 3rd Conference on Intelligent Systems, Modeling, and Simulation (ISMS), pp. 283–286, 2012.
  29. Mahajan S. and Patil D., "Image Retrieval using Contribution-based Clustering Algorithm with Different Feature Extraction Techniques," Proc. Conference on IT in Business, Industry, and Government (CSIBIG), pp. 1-7, 2014.
  30. Stefan R.A., Szoke I.-A., and Holban S., "Hierarchical Clustering Techniques and Classification Applied in Content-based Image Retrieval," Proc. 10th IEEE Jubilee Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 147–152, 2015.
  31. Younus Z.S., Mohamad D., Saba T., Alkwaz M.H., Rehman A., Al-Rodhaan M., and et al., "Content-based Image Retrieval using PSO and K-means Clustering Algorithm," Arab J Geosci, vol. 8, pp. 6211-6224, 2015.
  32. Corel 1000 and Corel 10000 image database, available at: http://wang.ist.psu.edu/, last visited 2015.
  33. Choras R., Andrysiak T., and Choraś M., "Integrated Color, Texture, and Shape Information for Content-based Image Retrieval," Patt Anal Appl, vol. 10, no. 4, pp. 333–343, 2007.
  34. Arthur D. and Vassilvitskii S., "K-means++: The Advantages of Careful Seeding," Proc. Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Content-based image clustering Feature extraction K-means clustering

Powered by PhDFocusTM