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

Content based Structural Recognition for Image Classification using PSO Technique and SVM

by Abhishek Pandey, Anjna Jayant Deen, Rajeev Pandey
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 7
Year of Publication: 2014
Authors: Abhishek Pandey, Anjna Jayant Deen, Rajeev Pandey
10.5120/15218-3724

Abhishek Pandey, Anjna Jayant Deen, Rajeev Pandey . Content based Structural Recognition for Image Classification using PSO Technique and SVM. International Journal of Computer Applications. 87, 7 ( February 2014), 6-11. DOI=10.5120/15218-3724

@article{ 10.5120/15218-3724,
author = { Abhishek Pandey, Anjna Jayant Deen, Rajeev Pandey },
title = { Content based Structural Recognition for Image Classification using PSO Technique and SVM },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 7 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number7/15218-3724/ },
doi = { 10.5120/15218-3724 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:05:16.973350+05:30
%A Abhishek Pandey
%A Anjna Jayant Deen
%A Rajeev Pandey
%T Content based Structural Recognition for Image Classification using PSO Technique and SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 7
%P 6-11
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The issue of SVMs parameter optimization with particle swarm optimization (pso) provide the optimum solution. This new classification approach may be an efficient alternative, in existing paradigms. PSO technique work with high dimensional datasets and mixed attribute data. The structure of the image is recognized through PSO technique which provide optimized parameter for SVM. This approach determines the performance of image classification after structural recognition based on content of image and comparing the obtained results with those reported for various other classification approaches. PSO-SVM technique can be applied mixed-attribute, hyperspectral data, hyperdimension spaces & problem description spaces and it can also be a competitive alternative to well established classification techniques. The optimized process of data reduces the unclassified region of support vector machine and improves the performance of image classification. The feature of region of image is classified by PSO-SVM technique in inside the image. Cassified features are increase recogniztion ratio because the feature of image is optimized.

References
  1. J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. Neural Netw. , vol. 4, pp. 1942–1948, 1995.
  2. R. C. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Proc. 6th Int. Symp. Micromach. Human Sci. , pp. 39–43, 1995.
  3. Y. H. Shi and R. C. Eberhart, "Comparison between genetic algorithms and particle swarm optimization" in Proc. 7th Int. Conf. Evol. Program, LNCS 1447, pp. 611–616, 1998
  4. Q. Zhang, E. Izquierdo, A new approach to image retrieval in a multi-feature space, in: International Workshop on Image Analysis for Multimedia Interactive Services, 2006.
  5. Jianxin Zhou, Ke Gao and Jintao Li. " An effective method in Relevance Feedback with SVM. Journal of Computer-Aided Design& Computer Graphics", Vol. 19, No. 4, 2007.
  6. Y. D. Chun, N. C. Kim, and I. H. Jang, "Content-based image retrieval using multiresolution color and texture features," IEEE Trans. Multimedia, vol. 10, no. 6, pp. 1073–1084, Oct. 2008.
  7. D. N. F. Awang iskandar james a. Thom s. M. M. Tahaghoghi "content-based image retrieval using image regions as query examples" in Australian computer society 2008.
  8. Farid Melgani, Yakoub Bazi "Classification of Electrocardiogram Signals with Support Vector Machines and Particle Swarm Optimization" IEEE Transactions On Information Technology In Biomedicine, Vol. 12, No. 5, September 2008.
  9. S. Osowski, R. Siroic, T. Markiewicz, and K. Siwek, "Application of support vector machine and genetic algorithm for improved blood cell recognition," IEEE Trans. Instrum. Meas. , vol. 58, no. 7, pp. 2159–2168, Jul. 2009.
  10. P. Schnitzspan, M. Fritz, S. Roth, B. Schiele, Discriminative Structure Learning of Hierarchical Representations for Object Detection, Computer Vision and Pattern Recognition, IEEE Computer Society Conference on 0 (2009) 2238–2245. doi:http://doi. ieeecomputersociety. org/10. 1109/CVPRW. 2009. 5206544.
  11. C. H. Lin, R. T. Chen and Y. K. Chan, "A smart content-based image retrieval system based on color and texture feature", Image and Vision Computing vol. 27, pp. 658–665, 2009.
  12. Andrea Paoli, Farid Melgani, Edoardo Pasolli "Clustering of Hyperspectral Images Based on Multiobjective Particle Swarm Optimization" IEEE Transactions On Geoscience And Remote Sensing, Vol. 47, No. 12, December 2009
  13. Qin Da " A Hybrid PSO/ACO Algorithm for Land Cover Classification" in IEEE conference 2010. Heng chen1, zhicheng zhao1 'an effective relevance feedback algorithm for image retrieval" 978-1-4244-6853-9/10/ 2010 IEEE.
  14. LI Linyi and LI Deren "Fuzzy Classification of Remote Sensing Images Based on particle Swarm Optimization" in International Conference on Electrical and Control Engineering in 2010.
  15. Shuaishi Liu; Yantao Tian; Cheng Peng; Jinsong Li; "Facial expression recognition approach based on least squares support vector machine with improved particle swarm optimization algorithm "IEEE International Conference on Robotics and Biomimetics (ROBIO), 2010 pp: 399 – 404
  16. Fatima Ardjani, Kaddour Sadouni "Optimization of SVM Multiclass by Particle Swarm (PSO-SVM)" I. J. Modern Education and Computer Science, 2010, 2, 32-38 Published Online December 2010 in MECS (http://www. mecs-press. org/).
  17. He Yang, Qian Du "Particle swarm optimization-based dimensionality reduction for hyperspectral image classification", Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International , 24-29 July 2011, ISSN : 2153-6996 Page(s): 2357 – 2360.
  18. Siu-Yeung Cho. "Content based structural reconition for flower image classification. " 2011 IEEE . Division of engineering. The university of nottingm, china.
  19. Manimala Singha and K. Hemachandran: " Content Based Image Retrieval using Color and Texture", Signal & Image Processing : An International Journal (SIPIJ) Vol. 3, No. 1, February 2012
  20. Yu ZENG a, Jixian ZHANG a, J. L. van GENDEREN b, Guangliang WANG a, "SVM-based Multi-textural Image Classification and Its Uncertainty Analysis. " Chinese Academy of Surveying and Mapping, Beijing 100830, P. R. China; b University of Twente, 2012 International Conference on Industrial Control and Electronics Engineering.
  21. Felci Rajam, S. Valli :" A Survey on Content Based Image Retrieval" Life Sci J 2013; 10(2): 2475- 2487] (ISSN: 1097-8135). http://www. lifesciencesite. com 343.
  22. Yukun Bao n, ZhongyiHu,TaoXiong "A PSO and pattern search based memetic algorithm for SVMs parameters optimization. " 2013 Elsevier B. V, in Neurocomputing. Department of Management Science and Information Systems, School of Management, Huazhong University of Science and Technology, Wuhan 430074, PR China.
  23. Nabila Nouaouria a,n, MounirBoukadoum a, RobertProulx b "Particle swarm classification: A survey and positioning. " 2013 Elsevier B. V. Pattern Recognition. Contents available in journal homepage: www. elsevier. com/locate/pr. Department of Computer Science, CP. 8888, Succ. Centre-ville, Montreal, QC, Canada H3C 3P8.
  24. Mahdi Setayesh , Mengjie Zhang a, Mark Johnston "A novel particle swarm optimisation approach to detecting continuous, thin and smooth edges in noisy images. " 2013. Elsevier B. V. Pattern Recognition. Contents available in journal homepage: www. elsevier. com/locate/pr.
  25. Dehua Liu,HuiQian n, GuangDai,ZhihuaZhang " An iterative SVM approach to feature selection and classification in high-dimensional datasets. " 2013. Elsevier B. V. Pattern Recognition. Contents available in journal homepage: www. elsevier. com/locate/pr. College of Computer Science & Technology, Zhejiang University, Hangzhou 310027, China.
Index Terms

Computer Science
Information Sciences

Keywords

Structural recognition PSO technique SVM classifier image classification.

Powered by PhDFocusTM