CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 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

Hybrid based Semantic Image Annotation using SVM and DT

by Manochitra. S, Janice E J, Suganya S
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 21
Year of Publication: 2013
Authors: Manochitra. S, Janice E J, Suganya S
10.5120/11208-6284

Manochitra. S, Janice E J, Suganya S . Hybrid based Semantic Image Annotation using SVM and DT. International Journal of Computer Applications. 65, 21 ( March 2013), 19-23. DOI=10.5120/11208-6284

@article{ 10.5120/11208-6284,
author = { Manochitra. S, Janice E J, Suganya S },
title = { Hybrid based Semantic Image Annotation using SVM and DT },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 21 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number21/11208-6284/ },
doi = { 10.5120/11208-6284 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:19:26.685101+05:30
%A Manochitra. S
%A Janice E J
%A Suganya S
%T Hybrid based Semantic Image Annotation using SVM and DT
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 21
%P 19-23
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This system proposes an ontology based framework for the semantic search in the image annotation process. The main objective of this approach is to use ontology for the semantic search in the image retrieval process. The ontology based framework is developed to define the image space. This system proposes a construction of semantic based approach for image representation using SVM and decision tree classifiers for learning and retrieval of relevant images. So the performance is significantly enhanced by using the SVM and decision tree as a classifier for retrieving the similar images.

References
  1. Anne-Marie Tousch, Stephane Herbin, and Jean-YvesAudibert ," Semantic hierarchies for image annotation: A survey", Pattern Recognition Vol 45,pp. 333–345 2011.
  2. Carneiro, G. , Chan, A. B. , Moreno, P. J. , Vasconcelos, N. ,, "Supervised learning of semantic classes for image annotation and retrieval", IEEE Trans. Pattern Anal. Mach. Intell. , 2007 Vol. 29 No. 3, 394–410
  3. Deng, Y. , Manjunath, B. S. , " Unsupervised segmentation of color-texture regions in images and video" IEEE Trans. Pattern Anal. Mach. Intell. Vol. 23 No. 8,pp. 800–810, 2001.
  4. Dmitri V. Kalashnikov,Sharad Mehrotra,JieXu, and Nalini Venkatasubramanian,"A Semantics-Based Approach for SpeechAnnotation of Images" IEEE transactions on knowledge and data engineering, 2011 Vol. 23, No. 9, September 2011,pp. 1373-1387.
  5. Han, Y. , Qi, X. ,"A complementary SVMs-based image annotation system" In: Proceedings of the International Conference on Image Processing (ICIP'05), Genoa, Italy, pp. 1185–1188 2005.
  6. Mylonas, P. , Spyrou, E. , Avrithis, Y. , Kollias, S. , 2009. Using visual context and region semantics for high-level concept detection. IEEE Trans. Multimedia 11 (2), 229– sss243.
  7. N. Magesh, P. Thangaraj, 2010, "Semantic Image Retrieval Based on Ontology and SPARQL Query, IJCA, PP 12-16.
  8. N. Magesh, P. Thangaraj,(2011) An Image Retrieval System Based on Extensive Feature Set using ID3 Decision Tree Algorithm, (2012), European journal of scientific research Vol 89 No. 1,121-135.
  9. Ning Yu , Kien A. Hua, Hao Cheng,2012, "A Multi-Directional Search technique for image annotation propagation",Visual Communication, Vol. 33 pp. 237-244.
  10. Quinlan, J. R. , 1986. Induction of decision trees. Springer Machine Learning 1, 81–106.
  11. Quinlan, J. R. , 1996. Improved use of continuous attributes in C4. 5. J. Artificial Intelligence Res. 4, 77–90.
  12. Ruhan He, Naixue Xiong, Laurence T. Yang and Jong Hyuk Park ,(2011)' Using Multi-Modal Semantic Association Rules to fuse keywords and visual features automatically for Web image retrieval' Elsevier science, Information Fusion Vol. 12 , pp. 223-230.
  13. Yakup Yildirim, Adnan Yazici, and Turgay Yilmaz(2011)' Automatic Semantic Content Extractionin Videos using a Fuzzy Ontology and Rule-based Model' IEEE Transactions On Knowledge And Data Engineering,pp. 1-15.
  14. Zeng Chen, Jin Hou , Dengsheng Zhang , Xue Qin . ,2012. An annotation extraction algorithm for image retrieval. Elsevier Pattern recognition (33),pp. 1257-1268.
  15. Zhao, Y. , Zhao, Y. , Zhu, Z. , Pan, J. S. , 2008. MRS-MIL: minimum reference set based multiple instance learning for automatic image annotation. In: Proceedings of the International Conference on Image Processing (ICIP'08), San Diego, California, USA, pp. 2160–2163.
Index Terms

Computer Science
Information Sciences

Keywords

Automatic image annotation support vector machine Decision Tree learner ontology based retrieval Content Based Image Retrieval

Powered by PhDFocusTM