Call for Paper - October 2014 Edition
IJCA solicits original research papers for the October 2014 Edition. Last date of manuscript submission is September 20, 2014. Read More

Distributed Content based Image Retrieval using Navigation Pattern with Relevance Feedback

Print
PDF
IJCA Proceedings on International Conference on Research Trends in Computer Technologies 2013
© 2013 by IJCA Journal
ICRTCT - Number 1
Year of Publication: 2013
Authors:
Janarthanam. S
Narendran. P

Janarthanam. S and Narendran. P. Article: Distributed Content based Image Retrieval using Navigation Pattern with Relevance Feedback. IJCA Proceedings on International Conference on Research Trends in Computer Technologies 2013 ICRTCT(1):25-29, February 2013. Full text available. BibTeX

@article{key:article,
	author = {Janarthanam. S and Narendran. P},
	title = {Article: Distributed Content based Image Retrieval using Navigation Pattern with Relevance Feedback},
	journal = {IJCA Proceedings on International Conference on Research Trends in Computer Technologies 2013},
	year = {2013},
	volume = {ICRTCT},
	number = {1},
	pages = {25-29},
	month = {February},
	note = {Full text available}
}

Abstract

The image retrieval applications are designed to fetch required images from the image databases. Images are searched using textual query or images. The textual query based retrieval is performed with image annotations. The image features are used in the content based image retrieval process on the image database provides huge collection of images. Query image features are compared with the database image features. The similarity measures are used to select relevant images from the databases. Relevance feedbacks are collected from the users at the time of query processing. The feedbacks are maintained under the image database and used in subsequent image retrievals. The navigation pattern based relevance feedback model is limited with accuracy and scalability factors. So the content based image retrieval scheme is enhanced to perform image retrieval in a distributed parallel manner and clustering techniques are used to improve the speed and accuracy of image retrieval. This is achieved through a multiscale approach. A quantitative measure is suggested for segmentation evaluation. The goal is to impute the missing data in the presence of edges or boundaries and recover the image. Their performance is compared with another method that imputes the missing values using edge-preserving spatial smoothers with locally varying weights. The proposed system first detects onset and offsets, and then generates segments by matching corresponding onset images.

References

  • W. K. Pratt, Digital Image Processing 4thEdition, John Wiley & Sons, Inc. , LosAltos, California, 2007.
  • D. H. Kim and C. W. Chung, "Qcluster: Relevance Feedback Using Adaptive Clustering for Content-Based Image Retrieval," Proc. ACM SIGMOD, pp. 599-610, 2003.
  • D. Semenovich and A. Sowmya. "Tensor power method for efficient map inference in higher-order". In Proc. Int. Conf. Patt. Recog. , 2010
  • G. Chung, L. A. Vese,"Energy Minimization Based Segmen -tation and Denoising Using a Multilayer Level Set Approach," Energy Minimization Methods in Computer Vision and Pattern Recognition, vol. 3757/2005, pp. 439–455.
  • Allili, M. S. , Ziou, D. ,(2006), "Automatic color-texture image segmentation by using active contours". In: Proceedings of 1st IEEE International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, Xi'an, China, 26–27, LNCS 4153, pp. 495–504
  • Aksoy S, Haralick RM "Probabilistic vs. geometric similarity measure for image retrieval" IEEE Conf. Computer Vision and Pattern Recognition, South Carolina,2000.
  • Brunelli R, Mich O "Image retrieval by examples". IEEE Trans Multimedia 2(3):164–171-2000.
  • Moghaddam B, Zhou XS "Factorized local appearance models. International Conf. on Pattern Recognition", Quebec City, Canada, 2000.
  • Zhou XS, Huang TS , "Small sample learning during multimedia retrieval using Bias Map" IEEE Int. Conf. Computer Vision and Pattern Recognition, Hawaii,2001
  • Xu X. , Lee D. J. , Antani S. and Long L. R. , "A Spine Xray Image Retrieval System Using Partial Shape Matching", IEEE Trans. on Information Technology in Biomedicine, Vol. 12, No. 1, pp. 100-108, 2008.
  • Greenspan H. and Pinhas A. T. , "Medical Image Categorization and Retrieval for Pacs Using the Gmm-Kl Framework", IEEE Trans. on Information Technology in Biomedicine, Vol. 11, No. 2, pp. 190-202, 2007.
  • Pourghassem H. and Ghassemian H. ; "A Novel Hybrid Relevance Feedback Based on Euclidean Distance and Probability Function Similarity Measures in a Xray Medical Images Retrieval System", Proceeding of 16th Iranian Conference on Electrical Engineering,ICEE2008, Vol. 1, pp. 197-202, May 2008.
  • El-Naqa, Yang Y. , Galatsanos N. P. , Nishikawa R. M. and Wernick M. N. ; "A Similarity Learning Approach to Content-based Image Retrieval: Application to Digital Mammography", IEEE Transactions on Medical Imaging, Vol. 23, No. 10, pp. 1233-1244, 2004.
  • Ves E. d. , Domingo J. , Ayala G. and Zuccarello P. ; "A Novel Bayesian Framework for Relevance Feedback in Image Content-based Retrieval Systems", Pattern Recognition, Vol. 39, pp. 1622-1632, 2006 Zuccarello.
  • Pourghassem H. and Ghassemian H. ; "Content-based Medical Image Classification Using Spectral Features and Directional Histogram in Multi scale Space", Proc. of Int. Conf. on Biomedical Engineering(ICBME2008), Vol. 1, pp. 124-130, January. 2008.
  • Saha S. K. , Das A. K. and Chanda B. ; "Image Retrieval Based on Indexing and Relevance Feedback", PatternRecognition Letters, Vol. 28, pp. 357–366, 2007
  • Qin T. , Zhang X. D. , Liu T. Y. , Wang D. S. , Mab W. Y. and Zhang H. J. , "An Active Feedback Framework for Image Retrieval", Pattern Recognition Letters, Vol. 29, pp. 637–646, 2008.
  • Leóna T. , Zuccarellob P. , Ayalaa G. , deVesb E. and Domingoc J, "Applying Logistic Regression to Relevance Feedback in Image Retrieval Systems", Pattern Recognition, Vol. 40, pp. 2621- 2632, 2007.
  • Lehmann T. , Guld M. , Thies C. , Fischer B. , Spitzer K. , Keysers D. , Ney H. , Kohnen M. , Schubert H. and Wein B. B. ; "Content-based Image Retrieval in Medical Applications", Methods Inform. Med. , Vol. 43, No. 4, pp. 354-361, 2004.
  • Datta, R. , Joshi, D. , Li, J. , Wang, J. Z. : Image retrieval: Ideas, influences, and trends of the new age. ACM Comput. Survey. 2008.