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

An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques

by Doaa M. Alebiary, Noura A. Semary, Hala H. Zayed
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 158 - Number 7
Year of Publication: 2017
Authors: Doaa M. Alebiary, Noura A. Semary, Hala H. Zayed
10.5120/ijca2017912852

Doaa M. Alebiary, Noura A. Semary, Hala H. Zayed . An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques. International Journal of Computer Applications. 158, 7 ( Jan 2017), 34-39. DOI=10.5120/ijca2017912852

@article{ 10.5120/ijca2017912852,
author = { Doaa M. Alebiary, Noura A. Semary, Hala H. Zayed },
title = { An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 158 },
number = { 7 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume158/number7/26923-2017912852/ },
doi = { 10.5120/ijca2017912852 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:04:14.477182+05:30
%A Doaa M. Alebiary
%A Noura A. Semary
%A Hala H. Zayed
%T An Efficient Semantic Image Retrieval based on Color and Texture Features and Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 158
%N 7
%P 34-39
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Content-based Image Retrieval (CBIR) is retrieving the desired images from huge collections. The user queries are becoming very specific and traditional text-based methods cannot efficiently handle them. CBIR system retrieves the image via low-level features such as color, texture and shape. In this work, we propose CBIR system that retrieves images from a database based on the semantic features of them. Our methodology divide the query image into 100 regions. And then, extracts Features Vector from each region and label each one with the suitable concept like (Sky, Sand, Water, trunks, foliage, rocks,..., and Grass). The labeling process in performed semi-automatically using k-means clustering and KNN classification algorithms. The system has been evaluated by recall and precision measures and compared to other recent works. The results of the paper reflects the efficiency of the system for retrieving images with up to 98% recognition ratio.

References
  1. Al Sebaey, N.A.E.M., 2007. Gray image coloring using texture similarity measures.
  2. Del Bimbo, A., 1998, June. A perspective view on visual information retrieval systems. In Content-Based Access of Image and Video Libraries, 1998. Proceedings. IEEE Workshop on (pp. 108-109). IEEE.
  3. Gebejes, A. and Huertas, R., 2013, March. Texture characterization based on grey-level co-occurrence matrix. In Proceedings in Conference of Informatics and Management Sciences (No. 1).
  4. Helala, M.A.E., Selim, M.M. and Zayed, H.H., 2009, December. An image retrieval approach based on composite features and graph matching. In Computer and Electrical Engineering, 2009. ICCEE'09. Second International Conference on (Vol. 1, pp. 466-473). IEEE.
  5. Helala, M.A., Selim, M.M. and Zayed, H.H., 2012. A content based image retrieval approach based on principal regions detection. International Journal of Computer Science Issues, 9(4), pp.204-213.
  6. Hiremath, P.S. and Pujari, J., 2007, December. Content based image retrieval using color, texture and shape features. In Advanced Computing and Communications, 2007. ADCOM 2007. International Conference on (pp. 780-784). IEEE.
  7. Kumar, V. and Gupta, P., 2012. Importance of statistical measures in digital image processing. International Journal of Emerging Technology and Advanced Engineering, ISSN, pp.2250-2459.
  8. Li, J. and Wang, J.Z., 2008. Real-time computerized annotation of pictures. IEEE transactions on pattern analysis and machine intelligence, 30(6), pp.985-1002.
  9. Liu, Y., Zhang, D., Lu, G. and Ma, W.Y., 2007. A survey of content-based image retrieval with high-level semantics. Pattern recognition, 40(1), pp.262-282.
  10. Semary, N.A., Hadhoud, M.M., El Kilani, W.S. and Ismail, N.A., 2007, March. Texture recognition based natural gray images coloring technique. In 2007 National Radio Science Conference (pp. 1-12). IEEE.
  11. Serrano-Talamantes, J.F., Avilés-Cruz, C., Villegas-Cortez, J. and Sossa-Azuela, J.H., 2013. Self organizing natural scene image retrieval. Expert Systems with Applications, 40(7), pp.2398-2409.
  12. Vogel, J. and Schiele, B., 2006. Performance evaluation and optimization for content-based image retrieval. Pattern Recognition, 39(5), pp.897-909.
  13. Vogel, J. and Schiele, B., 2007. Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision, 72(2), pp.133-157.
  14. Vogel, J., Schwaninger, A., Wallraven, C. and Bülthoff, H.H., 2006, July. Categorization of natural scenes: local vs. global information. In Proceedings of the 3rd symposium on Applied perception in graphics and visualization (pp. 33-40). ACM.
Index Terms

Computer Science
Information Sciences

Keywords

Big Data Content-Based Image Retrieval High-Level Semantics Semantic Gap.

Powered by PhDFocusTM