A REVIEW ON CONTENT BASED IMAGE RETRIEVAL
DOI:
https://doi.org/10.24297/ijct.v15i14.5640Keywords:
Image processing, Local binary pattern, cbir, opencvAbstract
Image retrieval means to recover the original image from the reconstructed image, here in this paper we have discussed latest techniques in the field of image retrieval for image processing. Content Based Image Retrieval (CBIR) is one of the most exciting and fastest growing research areas in the field of Image Processing. The techniques presented are Boosting image retrieval, soft query in image retrieval system, content based image retrieval by integration of metadata encoded multimedia features, and object based image retrieval and Bayesian image retrieval system. Some probable future research directions are also presented here to explore research area in the field of image retrieval
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References
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[3] H. J´egou, M. Douze, C. Schmid and P. P´erez, "Aggregating local descriptors into a compact image representation," IEEE, pp. 3304-3311, 2010.
[4] Y. Chen, J. . Z. Wang and R. Krovetz, "CLUE: Cluster-Based Retrieval of Images by Unsupervised Learning," IEEE, pp. 1187-1201, 2005.
[5] R. Fergus, L. Fei-Fei, P. Perona and A. Zisserman, "Learning Object Categories from Google’s Image Search," IEEE, 2005.
[6] Y. Chen, X. Li, A. Dick and A. v. d. Hengel, "Boosting Object Retrieval With Group Queries," IEEE, pp. 765-768, 2012.
[7] R. Arandjelovi´c and A. Zisserman, "Three things everyone should know to improve object retrieval," IEEE, pp. 2911-2918, 2012.
[8] M. Perd'och, Chum and J. Matas, IEEE, pp. 9-16, 2009.
[9] S. . A. Chatzichristofis and Y. S. Boutalis, "CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval," Springer-Verlag Berlin Heidelberg, pp. 313-322, 2008.
[10] Y. Chen and J. Z. Wang, "A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval," IEEE, pp. 1252-1267, 2002.
[11] C.-H. Lin, R.-T. Chen and Y.-K. Chan, "A smart content-based image retrieval system based on color and texture feature," ELSEVIER, p. 658–665, 2009.
[12] J. Z. Wang, J. Li and G. Wiederhold, "SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries," IEEE, pp. 947-963, 2001.
[13] S. Gandhani, R. Bhujade and A. Sinhal, "AN IMPROVED AND EFFICIENT IMPLEMENTATION OF CBIR SYSTEM BASED ON COMBINED FEATURES," IET, pp. 353-359.
[14] S. M. H. Khan, A. Hussain and I. F. T. Alshaikhl, "Comparative study on Content-Based Image Retrieval (CBIR)," IEEE, pp. 61-66, 2013.
[15] Sreedevi S. and Shinto Sebastian, "Fast Image Retrieval with Feature Levels," IEEE, 2013.
[16] S. Ezekiel, Mark G. Alford, David Ferris and Eric Jones,, "Multi-Scale Decomposition Tool for Content Based Image Retrieval," IEEE, 2013.
[17] K. Juneja, A. Verma , S. Goel and S. Goel , "A Survey on Recent Image Indexing and Retrieval Techniques for Low-level Feature Extraction in CBIR systems," IEEE, pp. 67-72, 2015.
[18] K. BELATTAR and S. MOSTEFAI, "CBIR using Relevance Feedback:Comparative Analysis and Major Challenges," IEEE, pp. 317-325, 2013.
[19] D. Jeyabharathi and A. Suruliandi , "Performance Analysis of Feature Extraction and Classification Techniques in CBIR," IEEE, pp. 1211-1214, 2013.
[20] H. Xie, Y. Ji and Y. Lu, "An Analogy-Relevance Feedback CBIR Method Using Multiple Features," IEEE, pp. 83-86, 2013.
[21] B. Kaur and S. Jindal, "An implementation of Feature Extraction over medical images on OPEN CV Environment".
[22] S. Kumar, S. Jain and T. Zaveri, "ARALLEL APPROACH TO EXPEDITE MORPHOLOGICAL FEATURE EXTRACTION OF REMOTE SENSING IMAGES FOR CBIR SYSTEM," IEEE, pp. 2471-2474, 2014.
[23] K. BELATTAR and S. MOSTEFAI , "CBIR with RF:which Technique for which Image," IEEE, 2013.
[24] G. Raghuwanshi and V. Tyagi, "Texture image retrieval using adaptive tetrolet transforms," ELSEVIER, pp. 1-8, 2015.
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Published
2017-01-17
How to Cite
A REVIEW ON CONTENT BASED IMAGE RETRIEVAL. (2017). INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(14), 7498–7503. https://doi.org/10.24297/ijct.v15i14.5640
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Research Articles
