CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Determination of Image Features for Content-based Image Retrieval using Interactive Genetic Algorithm

by Sharvari M. Waikar, K.b.khanchandani
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 89 - Number 17
Year of Publication: 2014
Authors: Sharvari M. Waikar, K.b.khanchandani
10.5120/15722-4583

Sharvari M. Waikar, K.b.khanchandani . Determination of Image Features for Content-based Image Retrieval using Interactive Genetic Algorithm. International Journal of Computer Applications. 89, 17 ( March 2014), 13-17. DOI=10.5120/15722-4583

@article{ 10.5120/15722-4583,
author = { Sharvari M. Waikar, K.b.khanchandani },
title = { Determination of Image Features for Content-based Image Retrieval using Interactive Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 89 },
number = { 17 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume89/number17/15722-4583/ },
doi = { 10.5120/15722-4583 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:09:29.443142+05:30
%A Sharvari M. Waikar
%A K.b.khanchandani
%T Determination of Image Features for Content-based Image Retrieval using Interactive Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 17
%P 13-17
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The development of content-based image retrieval (CBIR) system has become a significant research issue nowadays, as the digital image libraries and other multimedia databases are mounting very fast in the different areas. It is important to effectively and precisely retrieve the desired images from a large image database. Most of the approaches proposed are for finding the image features and some of the approaches include the user's subjectivity and preferences in the image retrieval process. This paper explains mainly about the determination of the image features like color, texture, and edge for the content-based image retrieval system which uses the interactive genetic algorithm. The color feature is extracted by using mean and standard deviation, the texture feature is extracted by using gray level co- occurrence matrix (GLCM) and the edge features of an image are extracted by using the edge histogram descriptor (EHD). Here the term interactive genetic algorithm (IGA) helps to reach more close to the user's need and satisfaction of image retrieval.

References
  1. Cho, Sung-Bae, and Joo-Young Lee. "A human-oriented image retrieval system using interactive genetic algorithm. " Systems, Man and Cybernetics, Part A: systems and humans, IEEE Transactions on 32. 3 (2002): 452-458.
  2. Lai, Chih-Chin, and Ying-Chuan Chen. "A user-oriented image retrieval system based on interactive genetic algorithm. " Instrumentation and Measurement, IEEE Transactions on 60. 10 (2011): 3318-3325.
  3. Khokher, Amandeep, and Rajneesh Talwar. "Content-based Image Retrieval: Feature Extraction Techniques and Applications. " Conference proceedings. 2012.
  4. Sindhu, S. , & Prakash, C. O. Development of the Content Based Image Retrieval Using Color, Texture and Edge Features?. International Journal of Computer Trends and Technology (IJCTT)–volume, 4, 1879-1884.
  5. Lu, T. C. , & Chang, C. C. (2007). Color image retrieval technique based on color features and image bitmap. Information processing & management, 43(2), 461-472.
  6. Pi, H. , Tong, C. S. , Choy, S. K. , & Zhang, H. (2006). A fast and effective model for wavelet subband histograms and its application in texture image retrieval. Image Processing, IEEE Transactions on, 15(10), 3078-3088.
  7. Park, Dong Kwon, Yoon Seok Jeon, and Chee Sun Won. "Efficient use of local edge histogram descriptor. " Proceedings of the 2000 ACM workshops on Multimedia. ACM, 2000.
  8. Cho, Sung-Bae. "Towards creative evolutionary systems with interactive genetic algorithm. " Applied Intelligence 16. 2 (2002): 129-138.
  9. Beligiannis, G. , Skarlas, L. , & Likothanassis, S. (2004). A generic applied evolutionary hybrid technique. Signal Processing Magazine, IEEE, 21(3), 28-38.
  10. Beligiannis, Grigorios N. , et al. "Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique. "Instrumentation and Measurement, IEEE Transactions on 54. 6 (2005): 2184-2190.
  11. Chang, Cheng-Yuan, and Deng-Rui Chen. "Active noise cancellation without secondary path identification by using an adaptive genetic algorithm. "Instrumentation and Measurement, IEEE Transactions on 59. 9 (2010): 2315-2327.
  12. Osowski, Stainslaw, et al. "Application of support vector machine and genetic algorithm for improved blood cell recognition. " Instrumentation and Measurement, IEEE Transactions on 58. 7 (2009): 2159-2168.
  13. Paravati, Gianluca, et al. "A genetic algorithm for target tracking in FLIR video sequences using intensity variation function. " Instrumentation and Measurement, IEEE Transactions on 58. 10 (2009): 3457-3467.
  14. da Silva, Sérgio F. , Marcos Aurélio Batista, and Célia A. Zorzo Barcelos. "Adaptive image retrieval through the use of a genetic algorithm. " Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on. Vol. 1. IEEE, 2007.
  15. Steji?, Zoran, Yasufumi Takama, and Kaoru Hirota. "Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns. "Information processing & management 39. 1 (2003): 1-23.
Index Terms

Computer Science
Information Sciences

Keywords

Content-based image retrieval (CBIR) color texture edge image features.

Powered by PhDFocusTM