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Content-based image retrieval using PSO and k-means clustering algorithm

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Abstract

In various application domains such as website, education, crime prevention, commerce, and biomedicine, the volume of digital data is increasing rapidly. The trouble appears when retrieving the data from the storage media because some of the existing methods compare the query image with all images in the database; as a result, the search space and computational complexity will increase, respectively. The content-based image retrieval (CBIR) methods aim to retrieve images accurately from large image databases similar to the query image based on the similarity between image features. In this study, a new hybrid method has been proposed for image clustering based on combining the particle swarm optimization (PSO) with k-means clustering algorithms. It is presented as a proposed CBIR method that uses the color and texture images as visual features to represent the images. The proposed method is based on four feature extractions for measuring the similarity, which are color histogram, color moment, co-occurrence matrices, and wavelet moment. The experimental results have indicated that the proposed system has a superior performance compared to the other system in terms of accuracy.

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References

  • Afifi AJ, and Ashour WM (2012) Content-based image retrieval using invariant color and texture features, in International Conference on Digital Image Computing Techniques and Applications (DICTA), IEEE: Fremantle, WA. p. 1–6

  • Ahmadyfard A. and Modares H (2008) Combining PSO and k-means to enhance data clustering. International Symposium on Telecommunications, p. 688 – 691.

  • Ahmed GF, Barskar R (2011) A study on different image retrieval techniques in image processing. Int J Soft Comput Eng (IJSCE) 1(4):247–251

    Google Scholar 

  • Al-Hamadani I (2006) Fast access image retrieval system, University of Mosul Iraq.

  • Alsabti K, Ranka S, Singh V (1997) An efficient k-means clustering algorithm. in 1st IN/IPPS/SPDP Workshop on High Performance Data Mining

  • Al-Taey SI (2006) Efficient content-based multimedia retrieval with neural network, in Computer science-Faculty of Computers & Mathematics Sciences, University of Mosul: Iraq.

  • Arai K, Rahmad C (2012) Wavelet based image retrieval method. Int J Adv Comput Sci Appl (IJACSA) 3(4):6–11

    Google Scholar 

  • Borkar AL (2012) Review of particle swarm optimization techniques International Journal of Innovative Research in Science & Engineering: p. 2347–3207.

  • Butt S, Tariq M (2013) Visual feature extraction for content-based image retrieval. Int J Acad Sci Res 1(3):1–10

    Google Scholar 

  • Chen J (2003) perceptually-based texture and color features for image segmentation and retrieval, University of Northwestern.

  • Chen Y, Wang J (2004) Image categorization by learning and reasoning with regions. J Mach Learn Res 5:913–939

    Google Scholar 

  • Chitkara, V., Color-based image retrieval using compact binary signatures, in Computing science 2001, University of Alberta: Edmonton, Alberta, Canada

  • Choras R, Andrysiak T, Choraś M (2007) Integrated color, texture and shape information for content-based image retrieval. Patt Anal Appl 10(4):333–343

    Article  Google Scholar 

  • Cui X, Potok T (2005) Document clustering analysis based on hybrid PSO + K-means algorithm. Journal of Computer Sciences, (Special Issue): p. 27–33.

  • Cui X. and Potok TE (2009) Swarm intelligence in text document clustering. Handbook of research on text and web mining technologies

  • Durai CRB, Duraisamy V, Vinothkumar C (2012) Improved content based image retrieval using neural network optimization with genetic algorithm. Int J Emerg Technol Adv Eng 2(7):400–403

    Google Scholar 

  • Ganesh SS et al (2012) Image retrieval using heuristic approach and genetic algorithm. J Comput Inform Syst 8(4):1563–1571

    Google Scholar 

  • Gudivada V, Raghavan V (1995) Content-based image retrieval systems. IEEE Comput 28(9):18–22

    Article  Google Scholar 

  • Haron H, Rehman A, Wulandhari LA, Saba T (2011) Improved vertex chain code based mapping algorithm for curve length estimation. J Comput Sci 7(5):736–743. doi:10.3844/jcssp.2011.736.743

    Article  Google Scholar 

  • Huang J (1998) Color-spatial image indexing and applications, University of Cornell: Faculty of the Graduate School.

  • Huang PW, Dai S (2003) Image retrieval by texture similarity. Pattern Recogn 36(3):665–679

    Article  Google Scholar 

  • Jensen Wong Jing Lung, Md. Sah Hj. Salam, A. Rehman, Mohd Shafry Mohd Rahim & T. Saba (2014) Fuzzy phoneme classification using multi-speaker vocal tract length normalization, IETE Technical Review, 31:2,128-136

  • Jhanwar N et al (2004) Content based image retrieval using motif co-occurrence matrix. Image Vis Comput 22:1211–1220

    Article  Google Scholar 

  • Kaur A, Singh MD (2012) An overview of PSO-based approaches in image segmentation. Int J Eng Technol 2(8):1349–1357

    Google Scholar 

  • Kekre HB, Gharge S (2010) Texture based segmentation using statistical properties for mammographic images. Int J Adv Comput Sci Appl (IJACSA) 1(5):102–107

    Google Scholar 

  • Komali A et al (2012) 3D color feature extraction in content-based image retrieval. Int J Soft Comput Eng (IJSCE) 2(3):560–563

    Google Scholar 

  • KosKela M (2003) Interactive image retrieval using self-organizing maps, in Computer Science and Engineering, University of Technology: Helsinki

  • Lee K. and El-Sharkawi M (2007) Modern heuristic optimization techniques: theory and applications to power systems: (IEEE Press Series on Power Engineering).

  • Lin C, Chen R, Chan Y (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27:658–665

    Article  Google Scholar 

  • Liu Y et al (2007) A survey of content based image retrieval with high-level semantics. J Pattern Recognit 40:262–282

    Article  Google Scholar 

  • Long F. et al (2010) Fundamentals of content based image retrieval, in Multimedia information retrieval and management: technological fundamentals and applications, multimedia signal processing book Feng, Siu, and Zhang, Editors. Springer-Verlag, Berlin Heidelberg New York p. 1–26

  • Ma L, Lin L, Gen M (2012) A PSO-SVM Approach for image retrieval and clustering, in the 41st International Conference on Computers & Industrial Engineering. p. 629–634.

  • Mishra AK, Das M, Panda TC (2013) Hybrid swarm intelligence technique for CBIR systems. Int J Comput Sci (IJCSI) 10(2):6–11

    Google Scholar 

  • Nirmal S (2009) Content based image retrieval techniques in 3rd National Conference, Bharati Vidyapeeth’s Institute of Computer Applications and Management, New Delhi: India

  • Rad AE, Mohd Rahim M, Rehman A, Altameem A, Saba T (2013) Evaluation of current dental radiographs segmentation approaches in computer-aided applications. IETE Tech Rev 30(3):210–222

    Article  Google Scholar 

  • Ramadan R, Abdel-Kader R (2009) Face recognition using particle swarm optimization-based selected features. Int J Signal Process Image Process Patt Recognit 2(2):51–66

    Google Scholar 

  • Rao MB, Rao BP, Govardhan A (2011) CTDCIRS: content based image retrieval system based on dominant color and texture features. Int J Comput Appl 18(6):40–46

    Google Scholar 

  • Rehman A, Saba T (2014) Features extraction for soccer video semantic analysis: current achievements and remaining issues. Curr Achiev Remain Issues Artif Intell Rev 41(3):451–461. doi:10.1007/s10462-012-9319-1

    Article  Google Scholar 

  • Rehman A, Mohammad D, SulongG (2009) T Saba, Simple and effective techniques for core-region detection and slant correction in offline script recognition Proceedings of IEEE International Conference on Signal and Image Processing Applications (ICSIPA’09), pp. 15–20.

  • Rui Y, Huang TS (1999) Image retrieval: current techniques, promising directions, and open issues. J Vis Commun Image Represent 10:39–62

    Article  Google Scholar 

  • Saba T, Rehman A, Elarbi-Boudihir M (2011). Methods and strategies on off-line cursive touched characters segmentation: a directional review, Artificial Intelligence Review, DOI 10.1007/s10462-011-9271-5.pp:45–54.

  • Saba T, Altameem A (2013) Analysis of vision based systems to detect real time goal events in soccer videos. Appl Artif Intell 27(7):656–667

    Article  Google Scholar 

  • Saba T, Rehman A (2012). Machine learning and script recognition, Lambert Academic publisher, pp:81–95

  • Salamah R (2010) Efficient content based image retrieval, in Computer Engineering, Islamic University – Gaza: Faculty of Engineering

  • Selamat A, Phetchanchai C, Saba T, Rehman A (2010) Index financial time series based on zigzag-perceptually important points. J Comput Sci 6(12):1389

    Article  Google Scholar 

  • Singh M, Singh S (2002) Spatial texture analysis: a comparative study. Pattern Recogn 1:676–679

    Google Scholar 

  • Varghese TA (2010) Performance enhanced optimization based image retrieval system. Int J Comput Appl (IJCA) 1:31–34

    Google Scholar 

  • Vora P, Oza B (2013) A survey on k-mean clustering and particle swarm optimization. Int J Sci Mod Eng (IJISME) 1(3):24–26

    Google Scholar 

  • Wang JZ (2012) Wang database; Available from: http://wang.ist.psu.edu/

  • Wang HH, Mohamad D, Ismail N (2009) Image retrieval: techniques, challenge, and trend. World Acad Sci Eng Technol 60:716–718

    Google Scholar 

Download references

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no. RGP-264.

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Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Zeyad Safaa Younus, Dzulkifli Mohamad & Mohammed Hazim Alkawaz

  2. Faculty of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq

    Zeyad Safaa Younus & Mohammed Hazim Alkawaz

  3. College of Computer and Information Sciences, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia

    Tanzila Saba

  4. MIS Department CBA, Salman bin Abdulaziz University, Alkharj, Kingdom of Saudi Arabia

    Amjad Rehman

  5. Computer Science Dept., College of Computer & Information Sciences, King Saud University, Riyadh, Kingdom of Saudi Arabia

    Mznah Al-Rodhaan & Abdullah Al-Dhelaan

Authors
  1. Zeyad Safaa Younus
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  2. Dzulkifli Mohamad
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  3. Tanzila Saba
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  4. Mohammed Hazim Alkawaz
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  5. Amjad Rehman
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  6. Mznah Al-Rodhaan
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  7. Abdullah Al-Dhelaan
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Correspondence to Amjad Rehman.

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Younus, Z.S., Mohamad, D., Saba, T. et al. Content-based image retrieval using PSO and k-means clustering algorithm. Arab J Geosci 8, 6211–6224 (2015). https://doi.org/10.1007/s12517-014-1584-7

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  • DOI: https://doi.org/10.1007/s12517-014-1584-7

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