Relevance Feedback Techniques Implemented in CBIR: Current Trends and Issues

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2014 by IJETT Journal
Volume-10 Number-4
Year of Publication : 2014
Authors : Dr. (Mrs) Ananthi Sheshasaayee , Jasmine .C
  10.14445/22315381/IJETT-V10P232

MLA 

Dr. (Mrs) Ananthi Sheshasaayee , Jasmine.C . "Relevance Feedback Techniques Implemented inCBIR: Current Trends and Issues", International Journal of Engineering Trends and Technology (IJETT), V10(4),166-175 April 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Abstract

The semantic gap problem and the performance accuracy issues in a Content Based Image Retrieval System (CBIR) can be efficiently overcome by the Relevance Feedback mechanism. Based on this feedback the CBIR system modifies its retrieval mechanism in an attempt to return the desirable output. In designing a Relevance Feedback (RF) mechanism a number of design requirements have to be considered that helps the CBIR system to function efficiently. In this paper the different RF techniques will be analysed by their performance and will throw light on the latest feedback algorithms and their related issues.

References

[1] Dr. Fuhui Long, Dr. Hongjiang Thang and Prof. David Dagan Feng. “Fundamentals of Content Based Image Retrieval”.
[2] J. Peng, B. Bhanu, and S. Qing, Probabilistic feature relevance learning for content-based image retrieval, Comput. Vision Image Understand. 75, 1999, 150–164.
[3] Sean D. MacArthur, Carla E. Brodley, and Avinash C. Kak. ”Interactive Content-Based Image Retrieval Using Relevance Feedback”, Computer Vision and Image Understanding 88, 55–75 (2002) doi:10.1006/cviu.2002.0977.
[4] Kranthi Kumar, K, Sunil Bhutada, V.V.S.S.S. Balaram, “An Adaptive Approach to Relevance Feedback in Cbir Using Mining Techniques”.
[5] Xiaofie He, Wei-Ying Ma, Oliver King, Mingjing Li, Hongjiang Zhang, “ Learning and Inferring a Semantic Space from User’s Relevance Feedback for Image Retrieval”, Multimedia ’02, December 1-6, 2002, Juan-les-pins, France.
[6] Pusha B. Patil, Manesh B. Kokare, “Relevance Feedback in Content Based Image Retrieval: A review”, Journal of Applied Computer Science & Mathematics, No. 10(5)/2011, Suceava.
[7] Bart Thomee, Michael S. Lew, “Interactive search in image retrieval: a survey”, International Journal of Multimedia and Information Retrieval (2012) 1:71-86.
[8] Junwei Han, King H. Mingjing Li, Hong-Jiang Zhang, “ A Memory learning Framework for Effective Image Retrieval”, IEEE Trans. On Image Processing, Vol 14, no.4. pp. 511-523.
[9] Wei Jiang, Guihua Er, Qionghai Dai, Jinwei Gu, “Hidden annotation for image retrieval with long-term relevance feedback learning”, Pattern Recognition 35 (2005) 2007-2021.
[10] Puja Kumar, “Image Retrieval Relevance Feedback algorithms: Trends and Techniques”, International Journal of Scientific Engineering and Technology, Volume No.2, Issue No.1, pp: 13-21, (ISSN: 2277-1581), 1 Jan. 2013.
[11] Ch.Srinivasa Rao, S.Srinivas Kumar and Chandra mohan, “Content Based Image Retrieval Using Exact Lengendre moments and Support Vector Machine”, The International Journal of Multimedia & its Applications (IJMA), Vol 2, No.2, May 2010.
[12] Fabio F. Faria, Adriano Veloso, Humberto M. Almedia, Eduardo Valle, Ricardo Da S. Torres and Marcos A. Goncalves, “Learning to Rank for Content-Based Image Retrieval”, MIR’ 10, Mar20-31, 2010 Philadelphia, Pennsylvania, USA.
[13] Sonali Jain, Satyam Shrivastava, “A novel approach for image classification in Content Based Image Retrieval using Support Vector Machine”, Intermational Journal of Computer Science & Engineering (IJCSET) Vol.4 No.03, March 2013.
[14] Xiaohong Yu and hong Liu, “Image Semantic Classification Using SVM in Image Retrieval”, Proceedings of the Second Symposium International Computer Science and computational Technology (ISCSCT, ’09) Huangshan, P. R.China, 26-28, Dec.2009, pp458-461.
[15] Swati Killikatt, Vidya Kulkarni, Madhuri Bijjal, “Content Based Image Retrieval by Online and Offline”, International Journal of Advanced Research in Electronics and Instrumentation Engineering”, Vol 2, Issue7, July 2013.
[16] Wei Jiang, Guihua Er., Qionghai Dai, Jinwei Gu, “Similarity – Based Online Feature Selection in Content – Based Image Retrieval”, IEEE Transactions on Image Processing, Vol 15. No.3, March 2006.
[17] Ritendra Datta Dhiraj joshi, Jia Li and James Z. Wang, “Image Retrieval: Ideas, Influences and Trends of the New Age”, ACM Computing Surveys, Vol 40, No. 2, Article 5, Pub. Date april 2008.
[18] Ritendra Datta, Jia Li, James Z. Wang, “Content-Based Image Retrieval – Approaches and Trends of the New Age”, The Pennsylvania State University, University Park, PA 16802, USA.
[19] Dacheng Tao and Xiaoou Tang, “Nonparametric Discriminant Analysis in Relevance Feedback for Content – Based Image Retrieval”, The Chinese University of Hong Kong.
[20] Lining zhang, Lipo Wang, Weisi Lin, “A semantic subspace learning method to exploit relevance feedback log data for image retrieval”, Computational Intelligence and Data Mining (CIDM), 2013 IEEE symposium, pages 178-183.
[21] Yufeng Zhao, Yao zhao, Zhenfeng Zhu, “ Relevance Feedback based on Query Refining and Feature Database Updating in CBIR System”, National Science Foundation No. 60172062.
[22] Shanmugapriya N. and R. Nallusamy, “A New Content Based Image Retrieval System Using GMM and Relevance Feedback”, journal of Computer Science 10(2): 330-340, 2014.
[23] Suman karthik, C.V. Jawahar, “Analysis of Relevance feedback in Content Based Image Retrieval”.
[24] Mohammed Lamine Kherfi and Djemel Ziou, “Relevance Feedback for CBIR: A new Approach based on Probabilistic Feature Weighting with Positive and Negative Examples”, IEEE Transactions on Image Processing, Vol15, No.4, April 2006.
[25] Carsten Tusk, Krsystoz Koperski, Selim Aksoy and Giovanni Marchisio, “Automated Feature Selection Through Relevance Feedback”, IEEE 2003.
[26] Jaume Amores, Nicu Sebe, Petia Radeva, Theo Gevers, Arnold Smeulders, “Boosting Contextual Information in Content-Based Image Retrieval”, ACM, 2004.
[27] Jie Yu, Yijuan Lu, Yuning Xu, Nicu Sebe and Qi Tian, “ Integrating Relevance Feedback in Boosting for Content-based Image Retrieval”, Department of Computer Science, University of Amesterdam, The Netherlands.
[28] Sean D. MacArthur, Carla E. Brodley, Chi-Ren Shyu, “ Relevance Feedback Decision Trees in Content-Based Image Retireval”, Proceedings of the IEEE workshop on Content – Based Access of Image and Video Libraries, 2000.
[29] Ying Liu, Dengsheng Zhang, Guojun Lu, “Region-Based image retrieval with high-level semantics using decision tree learning”,Pattern Recognition 41 (2008) 2554-2570.
[30] Nenad S. Kojic, Slobodan K.Cabarkapa, Goran J. Zajic and Branimir D. Rejlin, “Implementation of neural Network in CBIR Systems with Relevance Feedback”, Journal of Automatic Control, University of Belgrade, Vol 16:41-45, 2006.
[31] Sharooz Nematipour, Jamshid Shanbehzadeh, Reza Askari Moghadam, “Relevance Feedback Optimization in Content Based Image Retrieval Via Enhanced Radial Basis Function Network”, proceedings of the International multiConference of Engineers and Computer Scientists 2011, Vol I, IMECS 2011, March 16-18, 2011, Hong Kong.
[32] Kranthi Kumar, Dr. T. Venu Gopal, K. Dasaradha Ramiah, Dr. P. Sammulal, “A Novel Approach to Optimize Relevance Feedback in CBIR via Integerating MSD and FRB Function Network”, 2nd International Conference on Innovative Research in Engineering and Technology (iCIRET2013), January3-5, 2013.
[33] B. Veera Jyothi, Dr. Uma Shanker, “Neural Network approach for Image Retrieval based on preference elicitation”, International Journal on Computer Science and Engineering, Vol 02, No. 04, 2010, 934-941.
[34] Ying Liu, Dengsheng Zhang, Guojun Lu, Wei-Yung ma, “A survey of content-based image retrieval with high semantics”, Pattern Recognition, 40(2007) 262-282.
[35] Darshana Mistry, “Survey of Relevance Feedback methods in Content-Based Image Retrieval”, International Journal of Computer Science & Engineering Technology (IJCSET), ISSN: 2229-3345.
[36] C.Ramesh Babu durai, V. Duraisamy, C.Vinothkumar, “Improved Content Based Image Retrieval Using Neural Network Optimization with Genetic Algorithm”, International Journal of Emerging Technology and Advanced Engineering, Vol2, Issue 7, July 2012.
[37] Hamed Modaghegh, Malihe Javidi, Hadi Sadoghi Yazdi and Hamid Reza Pourreza, “Learning of Relevanc Feedback Using a Novel kernel Based Neural network”, Australian Journal of Basic and Applied Sciences, 4(2): 171-186, 2010.
[38] Sasi Kumar and Y.S. Kumaraswamy, “A Boosting Frame Work for Improved Content Based Image Retrieval”, Indian Journal of Science and Technology.
[39] Dr. V.V.S.S.S.Balaram, Kranthi kumar and Sunil Bhutada, “An Integrated Relevance Feedback Method for CBIR using Histogram Values, Texture Descriptor and Interactive Boosting”, AP,India.
[40] Suraj M. Gulhane, Akash G. Shinde and Amit kumar Singh, “Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns”, journal of Engineering, Computers & Applied Sciences (JEC & AS), Volume 2, No.3, march 2013. ISSN no. 2319-5606.
[41] Bhailal Limbasiya and Swati Patel, “Content Based Image Retrieval with Log based Relevance Feedback Using Combination of Query Expansion and Query Point Movement”, International Journal of Computer Science and Mobile Computing, IJCSMC, Vol2, Issue 5, may 2013, pg. 155-161.
[42] B. Dinakarn, J. Annapurna and Ch. Aswani Kumar, “Interactive Image Retrieval Using Text and Image Content”, Cybernrtics and Information Technologies, Vol 10. No.3. 2010.
[43] Abolfazl Llakdashti and Hossein Ajorloo, “Content – Based Image Retrieval Based on Relevance Feedback and Reinforcement Learning for medical Images”, ETRI Journal, Vol. 33, Number 2, April 2011.
[44] Latika Pinjarkar, Manisha Sharma and Kamal Mehta, “Comparative Evaluation of Image Retrieval Algorithms using Relevance Feedback and its Applications”, International Journal of Computer Applications (0975- 888), Volume 48 – No. 18, June 2012.
[45] Karthik S, Snehanshu Saha and Chaitra G., “A New Relevance Feedback based Approach for Efficient Image Retrieval”, International Journal of Computer Applications (0975 – 8887), Volume 61 – No.14, January 2013.

Keyowrds
CBIR, Relevance Feedback mechanism, semantic gap, RF Techniques.