A Review on Different Categories of CBIR Methods

Authors(2) :-D. Latha, Dr. Y. Jacob Vetha Raj

Content based image retrieval (CBIR) has become a main research area in multimedia applications. In the literature, there is lot of papers focusing on the content-based image retrieval in order to extract the semantic information within the query concept. The CBIR methods used in image searching areas differ by the user interaction and processing style in query image input. If we classify the CBIR methods into different categories, they are useful for deciding the suitable method for suitable environment and application. This paper classifies the different category of CBIR methods based on the user interaction with query image and the query processing style. In this paper, a review on nine categories of CBIR methods are given and for each categories a significant count of latest papers are described to enhance the better understanding of the users. This paper highlights the best CBIR methods for every user specific requirements.

Authors and Affiliations

D. Latha
Department of PG Computer Science, Nesamony Memorial Christian College, Marthandam, Tamilnadu, India
Dr. Y. Jacob Vetha Raj
Department of Computer Science, Nesamony Memorial Christian College, Marthandam, Tamilnadu, India

Query, Database, Image Retrieval and Texture.

  1. H,R. Tizhoosh, "Barcode annotations for medical image retrieval: A preliminary investigation", IEEE international conference, Doi:978-1-4799-8339-1/15, 2015.
  2. Konstantinos A. Reftopoulos, Klimis S.Ntalianis, Dionyssios D.Sourlas and Stefanos D. Kollias, "Mining user queries with Markov Chains: application to online image retrieval", IEEE transactions on knowledge and data engineering, Vol-25, No-2, Feb 2013.
  3. Hanjiang Lai, Pan Yan, Xiangbo Shu, Yunchao Wei and Shuicheng Yan, "Instance aware Hashing for multi label image retrieval", IEEE transactions on image processing, Doi: 10.1109/ TIP.2016.2545300, 2016.
  4. X.Yu, H.Luo and Z.M.Lu, "Color image retrieval using pattern co-occurrence matrices based on BTC and VQ", Electronics letter, Vol-47, No-2, Jan 2011.
  5. Franca Debole, Claudio Gennaro and Pasquale Savino, "Enriching image feature description supporting effective content based retrieval and annotation," IEEE international conference, 2014.
  6. Siddhivinayak Kulkarni and Pradnya Kulkarni, "Color image annotation using hybrid intelligent techniques for image retrieval", IEEE international conference, Doi: 978-1-4673-5116-4/12, 2012.
  7. Schroth, S.Hilsenbeck, R.Huitl, F.Schweiger and E.Steinbach, "Exploiting text-related features for content based image retrieval", IEEE international symposium on multimedia, 2011.
  8. Xin Zhou, Adrien Depeursinge and Henning Muller, "Information fusion for combining visual and texual image retrieval", International conference on pattern recognition, 2010.
  9. Jesus Favela and Victoria Meza, "Image retrieval agent: integrating image content and text", IEEE intelligent systems, 1999.
  10. Maryam Taghizadeh and Abdolah Chalechale, "A novel method for multiple-query image retrieval", SPIS2015, Tehran, Iran, 2015.
  11. Ko-Jen Hsiao, Jeff Calder and Alfred O.Hero, "Pareto-Depth for multiple query image retrieval", IEEE Transactions on image processing, Vol-24, No.2 , 2015.
  12. E., Ramakrishnan.K, Navya Nandakumar and Poulose Jacob.K, "An efficient multi query system for content based image retrieval using query replacement," IEEE Conference, SNPD, 2015.
  13. Behjat Siddiquie, Rogerio S. Feris and Larry S .Davis, "Image ranking and retrieval based on multi attribute queries", IEEE conference, 2011.
  14. Vel murugan. M and Sam Mathew. M, "2D and 3D active shape model with surf algorithm for object retrieval: content based image retrieval", International conference on Advanced computing and communication systems, ICACCS, 2013.
  15. Jian Guo Wu and Xi Zhao Wang and Hong Jie Xing, "Regional objects based image retrieval", International conference, Doi: 978-1-4244-8738-7/11, 2011.
  16. Yan He, Lei Yang, Yichun Zhang, Xiaoyu Wu and Yun Chang, "The binary image retrieval based on the improved shape context", 7th international congress on image and signal processing, 2014.
  17. Nafaa Nacereddine, Savatore Tabbone, Djemel Ziou and Latifa Hamami, " Shape based image retrieval using a new descriptor based on the Radon and wavelet transforms", International conference on pattern recognition, 2010.
  18. Shan Li, Moon Chuen Lee and Chi-Man Pun, "Complex zernike moments features for shape based image retrieval", IEEE transactions on systems, MAN, and Cybernetics-Part-A: systems and humans, Vol-39, No.1, Jan. 2009.
  19. Mathias Eitz, Kristian Hildebrand, Tamy Boubekeur and Marc Alexa, "Sketch based image retrieval: Bench mark and Bag-of-features descriptors", IEEE transactions on visualization and computer graphics, Vol-17,No: 11, 2011.
  20. Shu Wang, Jian Shang, Tony. X. Han and Zhenjiang Miao, "Sketch based image retrieval through hypothesis driven object boundary selection with HLR descriptor", IEEE transactions of multimedia, Vol-17, No:7, July 2015.
  21. Da Pan, Ping Shi and Cuiying Li, "Sketch based image retrieval by using saliency", 11th international conference on fuzzy systems and knowledge discovery, 2014.
  22. Xiaohui Yang, Feiya Lv, Lijun Cai and Dengfeng, "Adaptive learning region importance for region based image retrieval", IET computer vision, Vol-9, Issue.3, PP 368-377, 2015.
  23. E.R and Poulos Jacob.K, "Image retrieval using color and texture features of regions of interest", IEEE conference, 2012.
  24. Yuber Velazco-Paredes, Rexana Flores-Quispe and Raquel E. Patino Escarcina, "Region based image retrieval using color and texture features on irregular regions of interest", IEEE COLCOM, 2015.
  25. Zhong Su, Hongjiang Zhang, Stan Li and Shaoping MA, " Relevance feedback in content based image retrieval: Bayesian Framework, Feature subspaces, and progressive learning", IEEE transactions on image processing, VOL-12, No.8, August 2003.
  26. Begum Demir and Lorenzo Bruzzone, "A novel active learning method in relevance feedback for content based remote sensing image retrieval", IEEE transactions on Geoscience and remote sensing, Doi: 10.1109/TGRS.2014.2358804, 2014.
  27. Steven C.H. Hoi, Michael R. Lyu and Rong Jin, "A unified log based relevance feedback scheme for image retrieval", IEEE transactions on Knowledge and data engineering, Vol-18, No.4, 2006.
  28. Dacheng Tao, Xiaoou Tang, Xuelong Li and Xindong Wu, "Asymmetric bagging and subspace for support vector machines based relevance feedback in image retrieval", IEEE transactions on pattern analysis and machine intelligence, Vol-28, No.7, 2006.
  29. Pushpa B.Patil and Manesh B.Kokare, "Content based image retrieval with relevance feedback using Riemannian manifolds", Fifth international conference on signal and image processing, 2014.
  30. Anelia Grigorova, Francesco G.B. De Natale, Charlie Dagli and Thomas S.Huang, "Content based image retrieval by feature adaptation and relevance feedback", IEEE transactions on multimedia, Vol-9, No.6, 2007.
  31. Patrick P. K. Chan, Zhi-Chun Huang, Wing W.Y. NG and Daniel S. Yeung, "Dynamic hierarchical semantic network based image retrieval using relevance feedback", Proceedings of the 2011 International conference on machine learning and cybernetics, Guilin, 2011.
  32. Dewen Zhuang and Shoujue Wang, "Content based image retrieval based on integrating region segmentation and relevance feedback", IEEE conference, Doi:978-1-4244-7874-3/10, 2010.
  33. Kamel Belloulata, Lakhdar Belallouche, Amina Belalia and Kidiyo Kpalma, "Region based image retrieval using shape adoptive DCT", IEEE Conference, Doi:978-1-4799-5403-2/14, 2014.
  34. Guo-Cyuan Chen and Chia-Feng Juang, "Fuzzy classifier with support vector learning for image retrieval using a specified object", IEEE international conference on systems, Man and Cybernetics (COEX), 2012.
  35. Syam and Y. Srinivasa Rao, "Integrating contourlet features with texture, color and spatial features for effective image retrieval", IEEE conference, Doi:978-1-4244-5265-1/10, 2010.
  36. Zhi-Chun Huang, Patrick P.K. Chan, Wing W.Y.NG and Daniel S.Yeung, "Content based image retrieval using color moment and Gabor texture feature", Proceedings of the nineth international conference on machine learning and cybernetics, 2010.
  37. Sugandha Agarwal, Ridhi Sharma and Rashmi Dubey, "Sketch based image retrieval using watershed transformation", Second international conference on computational intelligence & communication technology, 2016.
  38. Houssm Chatbri and Keisuke Kameyama, "Sketch based image retrieval by shape points description in support regions", IEEE conference, Doi:978-1-4799-5/13, 2013.
  39. Mas Rina Mustaffa, Fatimah Ahmad, Ramlan Mahmod and Shyamala Doraisamy, "Invariant generalized Ridgelet fourier for shape based image retrieval", IEEE conference Doi:978-1-4244-5651-2/10, 2010.
  40. Gokaramaiah, P.Viswanth and B.Eswara Reddy; "A novel shape based hierarchical retrieval system for 2D images", International conference on advances in recent technologies in communication and computing, 2010.
  41. Yen-Shin Lee, Shu-Sheng Hao, Shu-Wei Lin and Sheng-Yi Li, "Image retrieval by region of interest Motif co-occurrence matrix", IEEE international symposium on intelligent signal processing and communication systems (ISPACS), 2012.
  42. Tohid Sedghi, Majid Fakheri and Mahrokh F. Sha Yesteh, "Region and content based image retrieval using advanced image processing techniques", IEEE conference, Doi: 978-1-4244-9708-9/10, 2010.
  43. Adriana Kovashka and Kristen Grauman, " Attribute pivots for guiding Relevance feedback in image search", IEEE computer society, conference, Doi: 10.1109/ICCV.2013.44, 2013.
  44. Nhu-Van Nguyen and Alain Boucher, "Clusters based relevance feedback for CBIR: a combination of query movement and query expansion", IEEE conference, Doi: 978-1-4244-8075-3/10, 2010.
  45. Yu Zhang, Jianxin Wu, and Jianfei Cai, "Compact representation of high dimensional feature vectors for large scale image recognition and retrieval", IEEE transactions on image processing, Doi:10.1109/TIP.2016. 254 9360, 2016.

Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1093-1100
Manuscript Number : CSEIT1831256
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

D. Latha, Dr. Y. Jacob Vetha Raj, "A Review on Different Categories of CBIR Methods", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1093-1100, January-February-2018.
Journal URL : http://ijsrcseit.com/CSEIT1831256

Article Preview

Follow Us

Contact Us