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Learning Context Determination based on Relevance Feedback for Memory Recall

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 46 - Number 9
Year of Publication: 2012
Authors:
Bela Joglekar
Parag Kulkarni
10.5120/6938-9308

Bela Joglekar and Parag Kulkarni. Article: Learning Context Determination based on Relevance Feedback for Memory Recall. International Journal of Computer Applications 46(9):23-27, May 2012. Full text available. BibTeX

@article{key:article,
	author = {Bela Joglekar and Parag Kulkarni},
	title = {Article: Learning Context Determination based on Relevance Feedback for Memory Recall},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {46},
	number = {9},
	pages = {23-27},
	month = {May},
	note = {Full text available}
}

Abstract

The brain continuously receives information through various stimuli and processes this information through cognition. Patients suffering from short term memory loss (e. g. in Parkinson's disease, seizures or epileptic attacks), lose a short episode of memory. The originality of the research lies in retrieving back any lost cognition due to damage/disease by presenting context-specific sequence of images to the subject under study. The approach proposes mapping the lost memory episode to a corresponding set of stored ranked images which can help regain memory loss. A framework is presented for implementation of context determination through relevance feedback. A comprehensive overview and analysis of existing techniques is also presented for context based retrieval of images.

References

  • Godfried T. Toussaint, 'The use of Context in Pattern Recognition', Pattern Recognition, Vol. 10, pp 189-204
  • Sergio Francisco da Silva, Marcela Xavier Ribeiro, Joao do E. S. Batista Neto, Caetano Traina-Jr, Agma J. M. Traina, 'Improving the ranking quality of medical image retrieval using a genetic feature selection method', Decision Support Syst. , Journal of Science Direct, Elsevier 2011 10. 1016/j. dss. 2011. 01. 015
  • Jinshan Tang and Scott Acton, Department of Electrical and Computer Engineering, University of Visginia, Charlottesville, VA 22904-4743, USA 'An Image Retrieval Algorithm using Multiple Query Images', 0-7803-7946-2/03/$17. 00 02003 IEEE
  • Guo-Dong Guo, Anil K. Jain, Fellow, IEEE, Wei-Ying Ma, Member, IEEE, and Hong-Jiang Zhang, Senior Member, IEEE 'Learning Similarity Measure for Natural Image Retrieval With Relevance Feedback', IEEE Transactions On Neural Networks, Vol. 13, No. 4, July 2002
  • Miguel Arevalillo-Herráeza, FrancescJ. Ferria, JuanDomingob, 'A naive relevance feedback model for content-based image retrieval using multiple similarity measures', Journal of Science Direct, Elsevier 10. 1016/j. patcog. 2009. 08. 010 pp: 619 – 629
  • Y. Ishikawa, R. Subramanya, C. Faloutsos, 'Mindreader: querying databases through multiple examples', in: Proceedings of the 24th International Conference on Very Large Data Bases, VLDB, New York, USA, 1998, pp. 433–438.
  • Y. Rui, S. Huang, M. Ortega, S. Mehrotra, 'Relevance feedback: a power tool for interactive content-based image retrieval', IEEE Transaction on Circuits and Video Technology 8 (5) (1998) 644–655
  • G. Ciocca, R. Schettini, 'A relevance feedback mechanism for content-based image retrieval', Information Processing and Management 35 (1) (1999) 605–632.
  • S. Tong, E. Chang, 'Support vector machine active learning for image retrieval', in: ACM Multimedia Conference, ACM Press, Ottawa, Canada, 2001, pp. 107–118.
  • J. Laaksonen, M. Koskela, E. Oja, PicSOM: 'self-organizing image retrieval with MPEG-7 content descriptors', IEEE Transactions on Neural Networks 13 (4) (2002) 841–853.
  • Y. Chen, J. Wang, R. Krovetz, Clue: 'cluster-based retrieval of images by unsupervised learning', IEEE Transactions on Image Processing 14 (8) (2005) 1187–1201.
  • G. Giacinto, 'A nearest-neighbor approach to relevance feedback in content based image retrieval', in: Proceedings of the 6th ACM International Conference on Image and video retrieval (CIVR'07), ACM Press, Amsterdam, The Netherlands, 2007, pp. 456–463.
  • M. Arevalillo-Herráez, M. Zacarés, X. Benavent, E. de Ves, 'A relevance feedback CBIR algorithm based on fuzzy sets', Signal Processing: Image Communication 23 (7) (2008) 490–504.
  • Wan-Ting Su, Ju-Chin Chen, Jenn-Jier James Lien, 'Region-based image retrieval system with heuristic pre-clustering relevance feedback', Journal of Science Direct, Elsevier 10. 1016/j. eswa. 2009. 12. 015 pp: 4984–4998
  • Philippe Henri Gosselin, Matthien Cord, Sylvie Phylipp-Folignet, 2007. "Combining visual dictionary kernel based similarity and learning strategy for image category retrieval", computer vision and image understanding 110(2008) 403-417.
  • Tieu, K. and Viola, P. 2004. "Boosting Image Retrieval", International Journal of Computer Vision 56(1), 17-36.
  • Y. Rui et al. , "A relevance feedback architecture in content-based multimedia information retrieval systems," in Proc. IEEE Workshop Content- Based Access of Image and Video Libraries, 1997.
  • Yin, P. Y. , Bhanu, B. , Chang, K. C. , and Dong, A. 2005. 'Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning', IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1536-1551.
  • N. H. Bergboer , E. O. Postma, H. J. van den Herik. 2006. "Content based object detection in still images". Image and vision computing, 24 (2006) 987–1000.
  • Md. Farooque. "Image indexing and retrieval". Documentation Research and Training Centre Indian Statistical Institute Bangalore-560059, 2003
  • Christos Faloutsos . "ImageMap: An Image Indexing Method Based on Spatial Similarity". Dept. of Computer Science Carnegie Mellon University, 2001
  • Ji Zhu, " Multiclass AdaBoost". Department of Statistics, University of Michigan, Ann Arbor, MI 48109, 2006.
  • Z. Pawlak, 'Rough Sets', International Journal of Computer and Information Sciences, vol 11, pp. 341-356, 1982
  • Yailé Caballero,Delia Álvarez ,Rafael Bello and María M. García, 'Feature Selection Algorithms using Rough Set Theory', Seventh International Conference on Intelligent Systems Design and Applications
  • Yan Huang, Shulin Chen, 'An Algorithm of Attribute Reduction Based on Rough Sets', 2008 International Conference on Computer Science and Software Engineering
  • Li Shu-qing, Zhang Sheng-xiu, 'A Congeneric Multi-Sensor Data Fusion Algorithm and Its Fault Tolerance', IEEE ICCASM 2010
  • Susmitha Vekkot, Pancham Shukla, 'A novel architecture for Wavelet Based Image Fusion' World Academy of Science, Engineering and Technology57 2009
  • Chen Wu, Xiaohua Hu, Enbin Wang, University of Science and Technology, 'Combination of Granules, Rough Sets with Evidence Theory and Its Application in Incomplete Data Fusion for Belief Estimation'
  • Michael S. Lew, Nicu Sebe, Chabane Djearba Lifl, Ramesh Jain, 'Content-Based Multimedia Information Retrieval: State of the Art and Challenges'ACM Transactions on Multimedia Computing, Communications and Applications, Vol. 2, No. 1, February 2006, Pages 1–19.