Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

July Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the July 2021 Edition of the journal. The last date of research paper submission is June 15, 2021.

A Novel Image Retrieval Technique based on Gabor Function, Local Tetra Pattern and ASMC

Kiran Ashok Bhandari, Ramachandra Manthalkar. Published in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Kiran Ashok Bhandari, Ramachandra Manthalkar
10.5120/ijais2016451582
Download full text
  1. Kiran Ashok Bhandari and Ramachandra Manthalkar. A Novel Image Retrieval Technique based on Gabor Function, Local Tetra Pattern and ASMC. International Journal of Applied Information Systems 11(3):38-45, August 2016. URL, DOI BibTeX

    @article{10.5120/ijais2016451582,
    	author = "Kiran Ashok Bhandari and Ramachandra Manthalkar",
    	title = "A Novel Image Retrieval Technique based on Gabor Function, Local Tetra Pattern and ASMC",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "August 2016",
    	volume = 11,
    	number = 3,
    	month = "Aug",
    	year = 2016,
    	issn = "2249-0868",
    	pages = "38-45",
    	numpages = 8,
    	url = "http://www.ijais.org/archives/volume11/number3/929-2016451582",
    	doi = "10.5120/ijais2016451582",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"
    }
    

Abstract

CBIR alone won’t give perfect retrieval results due to semantic gap. To overcome the problem of semantic gap in CBIR, more than one Semantic Content Based Image Retrieval techniques are required which is known as Hybrid Classification System. Hence the proposed approach uses multiple machine learning techniques with combination of different classifiers like supervised and unsupervised, soft classifiers, spectral contextual classifiers. Remotely Sensed Image Retrieval System (RSIR) has to identify and retrieve similar images based on query image, to do so we need to extract feature of image in order to compare query Image and database image. The proposed approach is a combination of two Phases. First Phase involves feature extraction by Texture Feature with the help of Gabor Function and Spectral Distribution using Advanced Split and Merge Clustering whereas second Phase identifies the Local Pattern of retrieved images in Phase-I.

The performance of the proposed approach is measured in terms of Precision, Recall and f-measure. Statistical analysis of the proposed hybrid approach in Phase-I (Texture and Spectral Distribution) shows that precision, recall and f-measure is get improved, on an average by 19.46%, 8.84%, 14.46% respectively when get compared with CBIR (Texture). Phase-I and Phase –II comparison in term of f-measure is increased up to 96.95%. Hence the hybrid approach gives more accurate result as compare to individual approach

Reference

  1. T. Bretschneider, R. Cavet, O. Kao, “Retrieval of remotely sensed imagery using spectral information content”, Proceedings of the InternationalGeosciences and Remote Sensing Symposium, vol. 4, pp. 2253– 2256, 2002.
  2. B. Liu, T. Bretschneider, “D-ISMC: A distributed unsupervised classification algorithm for optical satellite imagery”, Proceedings of theInternational Geosciences and Remote Sensing Symposium 2003, in press
  3. Y. Rubner, C. Tomasi, L.J. Guibas, “The Earth Mover’s Distance as a metric for image retrieval”, International Journal of Computer Vision, vol. 40, no. 2, pp. 99–121, 2000.
  4. O. Kao, T. Bretschneider, G.R. Joubert, “Image retrieval with Gabor- Wavelet- Networks”, Proceedings of the International Conference on Internet and Multimedia Systems and Applications, pp. 312–317, 2002.
  5. T.S. Lee, “Image representation using 2D Gabor wavelets”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 10, 959–971, 1996.
  6. Y. Rui, T. S. Huang, S. Mehrotra, “Content-based image retrieval with relevance feedback in MARS”, IEEE Transaction on Image Processing, vol. 9, pp. 102– 119, 2000.
  7. P. Muneesawang, L. Guan, “Interactive CBIR using RBF-based relevance feedback for WT/VQ coded images”, Proceedings of the IEEE International Conference on Acoustic, Speech and Signal Processing,vol. 3, pp. 1641–1644, 2001.
  8. H. Greenspan, G. Dvir, Y. Rubner, “Region correspondence for image matching via EMD f low”, Proceedings of IEEE Workshop on Contented- based Access of Image and Video Libraries, pp. 27–31, 2000.
  9. “SRBIR”, Journal of Computer Science, Felci Rajam et al., 2011a.
  10. Z. Guo, L. Zhang, and D. Zhang, “A completed modeling of local binary pattern operator for texture classification,” IEEE Trans. Image Process., Vol. 19, No. 6, pp. 1657–1663, June 2010.
  11. “Scale and Rotation Invariant Gabor Features for Texture Retrieval”, IEEE Conference DICTA, Rahman M.H. et al., 2011
  12. “Memory Learning Framework for Retrieval of Neural Objects” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Sanjeev S. Sannakki, Sanjeev P. Kaulgud, Volume 1, Issue 6, August 2012.
  13. Junwei Han, King N. Ngan, Fellow, IEEE, Mingjing Li, and Hong-Jiang Zhang, Fellow, IEEE, A Memory Learning Framework for Effective Image Retrieval. 2007.
  14. En cherg, Feng Jing, Chao, Lei Zhang , “search Result clustering based relevance feedback for web image retrieval”,IEEE,1-422-0728-1/07/2007
  15. H. J. Zeng, Q. C. He, Z. Chen, W. Y. Ma and J. W. Ma, “Learning to cluster Web search results," Proc. of the 27h annual international ACM SIGIR conference, pp. 210-217.
  16. Srikant S.K ,Dr T C Manjunath, “A Novel improved technique of image indexing for efficient content based image retrieval using local patterns”, IJETI-Volume 12 Number 9- Jun 2014
  17. Subramanian Murala, R.P.Maheshwari and R.Balasubramanian, “Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval”,IEEE Trans. Image Process., vol. 21, no. 5, pp. 2874–2886, MAY 2012.
  18. http://en.wikipedia.org/wiki/Information_retrieval.
  19. http://en.wikipedia.org/wiki/Content-based_image_retrieval.

Keywords

Local Tetra Pattern, Split and Merge Clustering, Gabour Function