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Random Walker Segmentation based Tag Completion for Image Retrieval
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 107 - Number 8
Year of Publication: 2014
|
10.5120/18770-0073 |
Shrikant Badghaiya and Atul Barve. Article: Random Walker Segmentation based Tag Completion for Image Retrieval. International Journal of Computer Applications 107(8):13-16, December 2014. Full text available. BibTeX
@article{key:article,
author = {Shrikant Badghaiya and Atul Barve},
title = {Article: Random Walker Segmentation based Tag Completion for Image Retrieval},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {107},
number = {8},
pages = {13-16},
month = {December},
note = {Full text available}
}
Abstract
Image retrieval is a technique of accessing texts or images from the web. Although there are various techniques implemented for the image retrieval such as using content based or tag based. Hence by using the technique for the image retrieval can be used in various fields. Tag based Image retrieval is a technique also used for the efficient retrieval of images [1]. Although the technique is efficient but it provides less accuracy, hence for the better access of the image retrieval based on tags segmentation is done and then matrix is generated to classify the images and hence can be retrieved in more accurate manner.
References
- Lei Wu, Rong Jin, and Anil K. Jain," Tag Completion for Image Retrieval", IEEE Transaction on Pattern Analysis and Machine Intelligence, 2013.
- Lei Wu, Rong Jin and Anil K. Jain "Tag Completion for Image Retrieval", IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 35, No. 3, pp. 716 – 727, March 2013.
- J. Li and J. Wang, "Real-time computerized annotation of pictures", IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 30, no. 6, pp. 985–1002, Jun. 2008.
- J. Jeon, V. Lavrenko, and R. Manmatha, "Automatic image annotation and retrieval using cross-media relevance models," in Proc. 21st Annu. Int. ACM SIGIR Conf. Research and Development in Information Retrieval, pp. 119–126, 2003.
- N. Abbadeni. An approach based on multiple representations and multiple queries for invariant image retrieval. In International Conference on Advances in Visual Information Systems, pages 570–579, Shanghai, China, June 2007.
- A. P. Natsev and J. R. Smith. "Active selection for multi-example querying by content" In IEEE International Conference on Multimedia and Expo, pages 445 – 448, Baltimore, USA, July 2003.
- X. S. Zhou and T. S. Huang. Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems, 8(6):536–544, April 2003.
- Y. Rui and T. S. Huang. Optimizing learning in image retrieval. In IEEE International Conference on Computer Vision and Pattern Recognition, pages 1236–1243, Hilton Head, USA, June 2000.
- X. Y. Jin and J. C. French. Improving image retrieval effectiveness via multiple queries. Multimedia Tools and Applications, 26(2):221–245, June 2005.
- R. Datta, D. Joshi, J. Li, and J. Z. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys, 40(2):5:1–5:60, 2008.
- A. Bosch, X. Munoz, and R. Marti. Which is the best way to organize/classify images by content? Image and vision computing, 25(6):778–791, June 2007.
- A. Bosch, A. Zisserman, and X. Munoz. Scene classification via plsa. In European Conference on Computer Vision, pages 517–530, Graz, Austria, May 2006.
- S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In IEEE International Conference on Computer Vision and Pattern Recognition, pages 2169–2178, New York, USA, June 2006.
- A. Vailaya, M. A. T. Figueiredo, A. K. Jain, and H. J. Zhang. Image classification for content-based indexing. IEEE Transactions on Image Processing, 10(1):117– 130, January 2001.
- H. Zhang, A. C. Berg, M. Maire, and J. Malik. SVM-KNN: Discriminative nearest neighbor classification for visual category recognition. In IEEE International Conference on Computer Vision and Pattern Recognition, pages 2126–2136, New York, USA, June 2006.