An Exhaustive vision and Prospects of Content-Based Image Retrieval
DOI:
https://doi.org/10.18486/ijcsnt/12.3.167Keywords:
CBIR,, Feature Extraction, Shape, Texture, Tamura, Wavelet Transform, Performance MeasureAbstract
Nowadays, work on digital images are gaining much popularity in the multimedia system, it becomes possible due to the rapid increase in the utilization of huge image database. Thus, the Content-Based Image Retrieval (CBIR) method has become the most valuable method for this purpose. This work mainly focuses on content-based image retrieval; which uses image features like color, shape, texture, etc. for searching the user query image from a large image database based on user request. CBIR is the most widely used technique as its searching capability is faster than the other traditional methods, and it works well in retrieving images automatically; it’s also a big alternative for the traditional method based approach. The CBIR techniques are used in many applications like surveillance detection, crime avoidance, fingerprint identification, E-library, medical, historical monument and biodiversity information systems and many more.
References
Alzubi, A. Amira, and N. Ramzan, “Semantic content-based image retrieval: A comprehensive study,” Journal of Visual Com-munication and Image Representation, vol. 32, pp. 20–54, 2015.
X. Li, T. Uricchio, L. Ballan, M. Bertini, C. G. Snoek, and A. D. Bimbo, “Socializing the semantic gap: A comparative survey on image tag assignment, refinement, and retrieval,” ACM Comput-ing Surveys (CSUR), vol. 49, no. 1, p. 14, 2016. DOI: https://doi.org/10.1145/2906152
Z. Lin, G. Ding, M. Hu, and J. Wang, “Semantics-preserving hashing for cross-view retrieval,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3864–3872. DOI: https://doi.org/10.1109/CVPR.2015.7299011
D. G. Lowe, “Distinctive image features from scale invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2,91–110, 2004. DOI: https://doi.org/10.1023/B:VISI.0000029664.99615.94
J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos,” in IEEE Conference on Computer Vision and Pattern Recognition, 2003, pp. 1470–1477. DOI: https://doi.org/10.1109/ICCV.2003.1238663
D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree,” in IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, 2006, pp. 2161–2168. DOI: https://doi.org/10.1109/CVPR.2006.264
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, “Object retrieval with large vocabularies and fast spatial matching,” in IEEE Conference on Computer Vision and Pattern Recognition, 22, 1–82007. DOI: https://doi.org/10.1109/CVPR.2007.383172
H. Jegou, M. Douze, and C. Schmid, “Hamming embedding and weak geometric consistency for large scale image search,” in European Conference on Computer Vision, pp. 304–317,2008. DOI: https://doi.org/10.1007/978-3-540-88682-2_24
W. Zhou, H. Li, Y. Lu, and Q. Tian, “Large scale image search with geometric coding,” in ACM International Conference on Multimedia, pp. 1349–1352 ,2011. DOI: https://doi.org/10.1145/2072298.2072012
O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman, “Total recall: Automatic query expansion with a generative feature model for object retrieval,” in International Conference on Computer Vision, pp. 1–8,2008.
J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, “Lost in quantization: Improving particular object retrieval in large scale image databases,” in IEEE Conference on Computer Vision and Pattern Recognition, , pp. 1–8,2008. DOI: https://doi.org/10.1109/CVPR.2008.4587635
O. Chum, J. Philbin, and A. Zisserman, “Near duplicate image detection: min-hash and tf-idf weighting,” in British Machine Vision Conference, vol. 3, p. 4, 2008. DOI: https://doi.org/10.5244/C.22.50
Z. Wu, Q. Ke, M. Isard, and J. Sun, “Bundling features for large scale partial-duplicate web image search,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 25–32, 2009. DOI: https://doi.org/10.1109/CVPR.2009.5206566
W. Zhou, Y. Lu, H. Li, Y. Song, and Q. Tian, “Spatial coding for large scale partial-duplicate web image search,” in ACM International Conference on Multimedia, pp. 511–520, 2010. DOI: https://doi.org/10.1145/1873951.1874019
O. Chum, A. Mikulik, M. Perdoch, and J. Matas, “Total recall II: Query expansion revisited,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 889–896, 2011. DOI: https://doi.org/10.1109/CVPR.2011.5995601
Y. Zhang, Z. Jia, and T. Chen, “Image retrieval with geometry-preserving visual phrases,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 809–816, 2011. DOI: https://doi.org/10.1109/CVPR.2011.5995528
X. Zhang, L. Zhang, and H.-Y. Shum, “Qsrank: Query-sensitive hash code ranking for efficient ǫ-neighbor search,” in IEEE Con-ference on Computer Vision and Pattern Recognition, pp. 2058– 2065, 2012. DOI: https://doi.org/10.1109/CVPR.2012.6247910
J. He, J. Feng, X. Liu, T. Cheng, T.-H. Lin, H. Chung, and S.-F. Chang, “Mobile product search with bag of hash bits and boundary reranking,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 3005–3012, 2012.
R. Arandjelovic and A. Zisserman, “Three things everyone should know to improve object retrieval,” in IEEE Conference on Computer Vision and Pattern Recognition, pp. 2911–2918, 2012. DOI: https://doi.org/10.1109/CVPR.2012.6248018
S. Zhang, M. Yang, T. Cour, K. Yu, and D. N. Metaxas, “Query specific fusion for image retrieval,” in European Conference on Computer Vision (ECCV), 2012. DOI: https://doi.org/10.1007/978-3-642-33709-3_47
Q. Tian, S. Zhang, W. Zhou, R. Ji, B. Ni, and N. Sebe, “Building descriptive and discriminative visual codebook for large-scale image applications,” Multimedia Tools and Applications, vol. 51, no. 2, pp. 441–477, 2011. DOI: https://doi.org/10.1007/s11042-010-0636-6
W. Zhou, H. Li, Y. Lu, and Q. Tian, “Large scale partial-duplicate image retrieval with bi-space quantization and geometric consis-tency,” in IEEE International Conference Acoustics Speech and Signal Processing, 2010, pp. 2394–2397. DOI: https://doi.org/10.1109/ICASSP.2010.5496205
S. Zhang, Q. Tian, G. Hua, Q. Huang, and S. Li, “Descriptive visual words and visual phrases for image applications,” in ACM International Conference on Multimedia, pp. 75–84, 2009. DOI: https://doi.org/10.1145/1631272.1631285
S. Zhang, Q. Huang, G. Hua, S. Jiang, W. Gao, and Q. Tian, “Building contextual visual vocabulary for large-scale image ap-plications,” in ACM International Conference on Multimedia, pp. 501–510, 2010. DOI: https://doi.org/10.1145/1873951.1874018
W. Zhou, Q. Tian, Y. Lu, L. Yang, and H. Li, “Latent visual context learning for web image applications,” Pattern Recognition, vol. 44, no. 10, pp. 2263–2273, 2011. Khodaskar and S. A. Ladhake, "A novel approach for content based image retrieval in context of combination S C techniques," Computer Communication and Informatics (ICCCI), 2015 International Conference on, Coimbatore,, pp. 1-6 ,2015.
Khodaskar and S. A. Ladhake, "A novel approach for content based image retrieval in context of combination S C techniques," Computer Communication and Informatics (ICCCI), 2015 International Conference on, Coimbatore,, pp. 1-6 ,2015
S. Pradeep and L. Malliga, "Content based image retrieval and segmentation of medical image database with fuzzy values," Information Communication and Embedded Systems (ICICES), 2014 International Conference on, Chennai, pp. 1-7, 2014.
J. M. Guo and H. Prasetyo, "Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding," in IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 1010-1024, March 2015.
Charmi Katira, Nikunj Vora, Krisha Wali, Abhinav Medhekar,” Advanced Content Based Image Retrieval Using Multiple Feature Extraction”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 4, Issue 10, October 2015.
Jun Yuea,b, Zhenbo Li b,1, Lu Liu b, Zetian Fub, “Content-based image retrieval using color and texture fused features”, Mathematical and Computer Modelling, Elsevier computers,Vol.55,l2011.
Nidhi Singh,Kanchan Singh, Ashok K. Sinha, “A Novel Approach for Content Based Image Retrieval”, Elsevier computer,Vol.4,pp. 245-250,2012. DOI: https://doi.org/10.1016/j.protcy.2012.05.037
Mutasem K. Alsmadi, " Mutasem K. Alsmadi, “An efficient similarity measure for content based image retrieval using memetic algorithm”, Elsevier computer,Vol.4,pp. 112-122,2017. DOI: https://doi.org/10.1016/j.ejbas.2017.02.004
A. Mohamed Uvaze Ahamed, C. Eswaran And R. Kannan, “CBIR System Based on Prediction Errors”, Journal Of Information Science And Engineering, Vol.33, pp. 347-365, 2017.
Mostafa G. Saeed, Fahad Layth Malallah, Zaid Ahmed Aljawaryy, “Content-Based Image Retrieval by Multi-Features Extraction and K-Means Clustering”, International Journal of Electrical, Electronics and Computers, Vol.2, Issue.3, May-Jun 2017. DOI: https://doi.org/10.24001/eec.2.3.1
Keyur D. Joshia, Sanket N. Bhavsar, Rajesh C. Sanghvi, “Image Retrieval System using Intuitive Descriptors”, 2nd International Conference on Innovations in Automation and Mechatronics Engineering ICIAME 2014, science direct, Procedia Technology ,Vol.14, pp. 535 – 542, 2014 . DOI: https://doi.org/10.1016/j.protcy.2014.08.068
Meenakshi Sharma & Anjali Batra, “Analysis of Distance Measures in Content Based Image Retrieval” , Global Journal of Computer Science and Technology: G Interdisciplinary, Vol. 14, Iss. 2, Ver. 1.0, 2014.
Yogita Mistry∗, D.T. Ingole, M.D. Ingole, “Content based image retrieval using hybrid features and various distance metric”, Journal of Electrical Systems and Information Technology, 2017. DOI: https://doi.org/10.1016/j.jesit.2016.12.009
Khodaskar and S. A. Ladhake, "A novel approach for content based image retrieval in context of combination S C techniques," Computer Communication and Informatics (ICCCI), 2015 International Conference on, Coimbatore,, pp. 1-6 ,2015 DOI: https://doi.org/10.1109/ICCCI.2015.7218060
S. Pradeep and L. Malliga, "Content based image retrieval and segmentation of medical image database with fuzzy values," Information Communication and Embedded Systems (ICICES), 2014 International Conference on, Chennai, pp. 1-7, 2014. DOI: https://doi.org/10.1109/ICICES.2014.7034091
J. M. Guo and H. Prasetyo, "Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding," in IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 1010-1024, March 2015. DOI: https://doi.org/10.1109/TIP.2014.2372619
Charmi Katira, Nikunj Vora, Krisha Wali, Abhinav Medhekar,” Advanced Content Based Image Retrieval Using Multiple Feature Extraction”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 4, Issue 10, October 2015.
Ekta Gupta and Rajendra Singh Kushwah,” Combination of Local, Global and k-mean using Wavelet Transform for Content Base Image Retrieval”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.8, No.6, pp.253-266, 2015. DOI: https://doi.org/10.14257/ijsip.2015.8.6.27
Neelima Bagri1 and Punit Kumar Johari,” A Comparative Study on Feature Extraction using Texture and Shape for Content Based Image Retrieval”, International Journal of Advanced Science and Technology Vol.80, pp.41-52, 2015. DOI: https://doi.org/10.14257/ijast.2015.80.04
Davar Giveki, Ali Soltanshahi, Fatemeh Shiri, Hadis Tarrah,” A New Content Based Image Retrieval Model Based on Wavelet Transform”, Journal of Computer and Communications, 3, 66-73,2015. DOI: https://doi.org/10.4236/jcc.2015.33012
P. Selvi Rajendran, R. Sandya, T. Yogeswari and G. Viveka,” Novel Image Retrieval Approach in Similarity Integrated Network Using Combined Ranking Algorithm”, World Engineering & Applied Sciences Journal 6 (3): 152-156, 2015.
S. Sasikala, R. Soniya Gandhi,” Efficient Content Based Image Retrieval System with Metadata Processing”, IJIRST –International Journal for Innovative Research in Science & Technology, Volume 1, Issue 10, March 2015.
Sandeep Singh , Er.Rachna Rajput,” Content Based Image Retrieval using SVM, NN and KNN Classification”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 6, June 2015.
T. Dharani and I. Laurence Aroquiaraj, “Content Based Image Retrieval”, International Conference on Pattern Recognition, Informatics and Mobile Engineering, February 21-22, 2013. DOI: https://doi.org/10.1109/ICPRIME.2013.6496719
W. M. Smeulders, M. Worring, S. Santini, A. Gupta and R. Jain, "Content-based image retrieval at the end of the early years," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec 2000
Tarulatha, N. Shroff and M. B. Chaudhary, "VIBGYOR indexing technique for image mining," 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), Ernakulam, 2016, pp. 191-193. DOI: https://doi.org/10.1109/SAPIENCE.2016.7684150
Ying Liua, Dengsheng Zhanga, Guojun Lua,” A survey of content-based image retrieval with high-level semantics”, Wei-Ying Mab aGippsland School of Computing and Information Technology, Monash University, Vic 3842, Australia bMicrosoft Research Asia, No. 49 ZhiChun Road, Beijing 100080, China
T. Barbu, "Content-Based Image Retrieval Using Gabor Filtering," 2009 20th International Workshop on Database and Expert Systems Application, Linz, pp. 236-240, 2009. DOI: https://doi.org/10.1109/DEXA.2009.61
Juneja K, Verma A and Goel S, “A Survey on Recent Image Indexing and Retrieval Techniques for Low-Level Feature Extraction in CBIR Systems”,IEEE International Conference on Computational Intelligence & Communication Technology (CICT), pp: 67 - 72, 2015. DOI: https://doi.org/10.1109/CICT.2015.92
S. M. Mukane, S. R. Gengaje and D. S. Bormane, “A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier”, Elsevier computers & Electrical Engineering, 2014. DOI: https://doi.org/10.1016/j.compeleceng.2014.06.006
MAW Hole and PL Ramteke, “Content based image retrieval using dominant color and texture features”, (IJCSIS) International Journal of Computer Science and Information Security, vol. 9, no. 2, 2011.
Samy Sadek, “Cubic-splines neural networkbased system for image retrieval”, Proceedings of the 16th IEEE international conference on Image processing (ICIP'09), Piscataway, NJ, USA, pp. 273-276, 2009. DOI: https://doi.org/10.1109/ICIP.2009.5413561
E. Horster, R. Lienhart, and M. Slaney, “Image retrieval on large-scale image databases,” in Proc. CIVR, pp. 17–24, 2007. DOI: https://doi.org/10.1145/1282280.1282283
Alghamdi, R.A., Taileb, M and Ameen. M, “A new multimodal fusion method based on association rules mining for image retrieval”, 17th IEEE Mediterranean Electrotechnical Conference (MELECON), pp: 493 – 499, 2014. DOI: https://doi.org/10.1109/MELCON.2014.6820584
Bing Wang, Xin Zhang, Miao Wang and Pu Zhao, “Saliency distinguishing and applications to semantics extraction and retrieval of natural image”, International Conference on Machine Learning and Cybernetics (ICMLC), vol.2, pp: 802 – 807, 2010. DOI: https://doi.org/10.1109/ICMLC.2010.5580581
Dengsheng Zhang, Islam, M.M, Guojun Lu and Jin Hou, “Semantic Image Retrieval Using Region Based Inverted File”, Digital Image Computing: Techniques and Applications, DICTA '09, pp.242 - 249, 2009. DOI: https://doi.org/10.1109/DICTA.2009.48
Ja-Hwung Su, Wei-Jyun Huang, Philip s. Yu and Vincent S. Tseng, “Efficient Relevance Feedback for Content Based Image Retrieval by Mining User Navigation Patterns”, IEEE Trans. On Knowledge and Data Engg., vol. 23, no. 3, pp.360-372, March 2011. DOI: https://doi.org/10.1109/TKDE.2010.124
Yu sun and Bir Bhanu, “Image retrieval with feature selection and relevance feedback”, Proceedings of 2010 IEEE 17th international conference on image processing, September 26-29, 2010. DOI: https://doi.org/10.1109/ICIP.2010.5651984
Lu Hui , “A Relevance Feedback System for CBIR with Long-Term Learning,” Proceedings of the 2010 International Conference on Multimedia Information Networking and Security (MINES '10), Washington, DC, USA, pp. 700-704, 2010. DOI: https://doi.org/10.1109/MINES.2010.151
S. A. Chatzichristofis, K. Zagoris, Y. S. Boutalis and N. Papamarkos, “Accurate Image Retrieval Based on Compact Composite Descriptors and Relevance Feedback Information”, IJPRAI, vol. 24, pp. 207-244, 2010. DOI: https://doi.org/10.1142/S0218001410007890
Wankhede, V.A. Mohod and P.S, “Content-based image retrieval from videos using CBIR and ABIR algorithm”, Global Conference on Communication Technologies (GCCT), pp 767 - 771, 2015. DOI: https://doi.org/10.1109/GCCT.2015.7342767
M. S. Sudhakar and K. Bhoopathy Bagan, “An effective biomedical image retrieval framework in a fuzzy feature space employing Phase Congruency and GeoSOM”, Elsevier Applied Soft Computing, vol. 22, pp.492 -503, 2014. DOI: https://doi.org/10.1016/j.asoc.2014.04.029
S. Mukhopadhyay, J. K. Dash and R. D. Gupta , “Content-based texture image retrieval using fuzzy class membership" , Elsevier Pattern Recognition Letters , vol. 34 , no. 6 , pp.646 -654 , 2013. DOI: https://doi.org/10.1016/j.patrec.2013.01.001
Y. Eissa, P.R. Marpu, I. Gherboudj, H. Ghedira, B.M.J. Taha Ouarda and Matteo Chiesa , “Artificial neural network based model for retrieval of the direct normal, diffuse horizontal and global horizontal irradiances using SEVIRI images” , Elsevier Solar Energy , vol. 89 , pp.1 -16 , 2013. DOI: https://doi.org/10.1016/j.solener.2012.12.008
Lin Chen, Dong Xu, Ivor W. Tsang, and Jiebo Luo, “Tag-Based Image Retrieval Improved by Augmented Features and Group-Based Refinement”, IEEE Trans. Multimedia, vol. 14, no. 4, Aug 2012. DOI: https://doi.org/10.1109/TMM.2012.2187435
Raghupathi Gali, M.L.Dewal and R.S.Anand, “Genetic algorithm for content based image retrieval” International Conference on Computation Intelligence, Communication Systems and Networks, 2012.
Linying Jiang, Jiefu Ren and Dancheng Li, “Content Based Image Retrieval algorithm oriented by Users Experience”, 7th International Conference on Computer Science & Education (ICCSE 2012), July 14-17, 2012. Melbourne, Australia. DOI: https://doi.org/10.1109/ICCSE.2012.6295116
Chih-Chin Lai, “A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm”, IEEE Transactions on Instrumentation and Measurement, 2011.
S. Feng, “Localized content-based image retrieval using saliency-based graph learning framework”, IEEE 10th International Conference on Signal Processing (ICSP), pp.1029-1032, 2010. DOI: https://doi.org/10.1109/ICOSP.2010.5655906
Gulfishan Firdose Ahmed, Raju Barskar, Jyoti Bharti and Ntin Singh Rajput, “Content Base Image Retrieval Using Fast Phong Shading”, Proceeding of IEEE, International Conference on Computational Intelligence and Communication Networks, CPS and indexed in IEEE Computer Society, pp.419-423, Novmber-2010, Bhopal, India. DOI: https://doi.org/10.1109/CICN.2010.86
F.Jeng, M.Li, H.-J. Zhang and B. Zhang, “An efficient and effective region-based image retrieval framework”, IEEE Transaction on Image Processing, May 2004. DOI: https://doi.org/10.1109/TIP.2004.826125
W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-based image retrieval at the end of the early years,” IEEE Trans. PAMI, vol. 22, no. 12, pp. 1349–1380, 2000. DOI: https://doi.org/10.1109/34.895972
Sheetal A. Wadhai , Seema S. Kawathekar, “Techniques of Content Based Image Retrieval: A Review”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727,75-79
Pradnya Vikhar, P.P. Karde and V.M. Thakare, “COMPREHENSIVE ANALYSIS OF SOME RECENT COMPETITIVE CBIR TECHNIQUES”, ictact journal on image and video processing, volume: 07, issue: 03, february 2017.
Anuradha Shitole and Uma Godase, “Survey on Content Based Image Retrieval” , International Journal of Computer-Aided Technologies (IJCAx) Vol.1,No.1,April 2014
Fuhui Long, Hongjiang Zhang and David Dagan Feng “Multimedia Information and Retrieval Management”, 1st Edition, Springer, 2003. DOI: https://doi.org/10.1007/978-3-662-05300-3_1
Mourad Oussalah, “Content based Image Retrieval: Review of State of Art and Future Directions”, Proceedings of 1st Workshop on Image Processing Theory, Tools and Applications, pp. 1-10, 2008. DOI: https://doi.org/10.1109/IPTA.2008.4743799
Pradnya A. Vikhar, “Content-Based Image Retrieval (CBIR): State-of-the-Art and Future Scope for Research”, IUP Journal of Information Technology, Vol. 6, No. 2, pp. 64-84, 2010.
Ahmad Alzu’bi, Abbes Amira and Naeem Ramzan, “SemanticContent-basedImageRetrieval:A Comprehensive Study”, Journal of Visual Communication and Image Representation, Vol. 32, pp. 20-54, 2015. DOI: https://doi.org/10.1016/j.jvcir.2015.07.012
M. Barrena, A. Caro, M. L. Durán, P. G. Rodríguez, J. P. Arias-Nicolas and T. Alonso, “Qatrisi Manager: A General Purpose CBIR System”, Machine Vision and Applications, Vol. 26, No. 6, pp. 423-442, 2015. DOI: https://doi.org/10.1007/s00138-015-0672-3
Jaehyun An, Sang Hwa Lee and Nam Ik Cho, “Content-based Image Retrieval using Color Features of Salient Regions”, Proceedings of IEEE International Conference on Image Processing, pp. 3042-3046, 2014. DOI: https://doi.org/10.1109/ICIP.2014.7025615
Remco C. Veltcamp and Mirela Tanase, “Content based Image Retrieval System: A Survey”, Technical Report, pp.1-62, 2002. DOI: https://doi.org/10.1007/978-1-4615-0987-5_5
Izem Hamouchene and Saliha Aouat, “A New Approach for Texture Segmentation Based on NBP Method”, Multimedia Tools Applications, Vol. 76, No. 9, pp. 1-20, 2015. DOI: https://doi.org/10.1007/s11042-015-3185-1
Shaoyan Sun, Wengang Zhou, Qi Tian and Houqiang Li, “Scalable Object Retrieval With Compact Image
Representation From Generic Object Regions”, ACM Transactions on Multimedia Computation, Communication Application, Vol. 12, No. 2, pp. 1-21, 2015. DOI: https://doi.org/10.1145/2818708
Kommineni Jenni, Satria Mandala and Mohd Shahrizal Sunar, “Content Based Image Retrieval Using Colour Strings Comparison”, Procedia Computer Science, Vol. 50, 374-379, 2014. DOI: https://doi.org/10.1016/j.procs.2015.04.032
Menglin Liu, Li Yang and Yanmei Liang, “A Chroma Texture Based Method in Color Image Retrieval”, Optik-International Journal for Light and Electron Optics, Vol. 126, No. 20, pp. 2629-2633, 2015. DOI: https://doi.org/10.1016/j.ijleo.2015.06.058
Michal Batko, Jan Botorek, Petra Budikova and Pavel Zezula, “Content-based Annotation and Classification Framework: A General Multi-Purpose Approach”, Proceedings of 17th International Database Engineering and Applications Symposium, pp. 58-67, 2013. DOI: https://doi.org/10.1145/2513591.2513651
Zhiyong Cheng, Jialie Shen and Haiyan Miao, “The Effects of Multiple Query Evidences on Social Image Retrieval”, Multimedia Systems, Vol. 22, No. 4, pp. 2456-2470, 2014. DOI: https://doi.org/10.1007/s00530-014-0432-7
Hanwang Zhang, Zheng-Jun Zha, Yang Yang, Shuicheng Yan, Yue Gao and Tat-Seng Chua, “Attribute-Augmented Semantic Hierarchy: Towards a Unified Framework for Content-Based Image Retrieval”, ACM Transactions on Multimedia Computing, Communications, and Applications, Vol. 11, No. 1, pp. 1-21, 2014. DOI: https://doi.org/10.1145/2637291
Alex Papushoy and Adrian G. Bors, “Image Retrieval based on Query by Saliency Content”, Digital Signal Processing, Vol. 36, pp. 156-173, 2015. DOI: https://doi.org/10.1016/j.dsp.2014.09.005
Romain Raveaux, Jean-Christophe Burie and Jean-Marc Ogier, “Structured Representations in a Content based Image Retrieval Context”, Journal of Visual Communication and Image Representation, Vol. 24, No. 8, 1252-1268, 2013 DOI: https://doi.org/10.1016/j.jvcir.2013.08.010
Emir Sokic and SamimKonjicija, “Novel Fourier Descriptor Based on Complex Coordinates Shape Signature”, Proceedings of 12th IEEE Transactions on International Workshop on Content-Based Multimedia Indexing, pp. 1-4¸ 2014. DOI: https://doi.org/10.1109/CBMI.2014.6849843
Cong Bai, Jinglin Zhang, Zhi Liu and Wan-Lei Zhao, “K-Means based Histogram using Multiresolution feature Vectors for Color Texture Database Retrieval”, Multimedia Tools Applications, Vol. 74, No. 7, pp. 1469-1488, 2015. DOI: https://doi.org/10.1007/s11042-014-2053-8
Anu Bala and Tajinder Kaur, “Local Texton XOR Patterns: A New Feature Descriptor for Content based Image Truncation Coding Features”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 25, No. 3,pp 466-481, 2015.
Min Huang, Huazhong Shu, Yaqiong Mab and Qiuping Gong, “Content-based Image Retrieval Technology using Multi-Feature Fusion”, Optik-International Journal for Light and Electron Optics, Vol. 126, No. 19, pp. 2144-2148, 2015. DOI: https://doi.org/10.1016/j.ijleo.2015.05.095
Suraya Abu Bakar, Muhammad Suzuri Hitam and Wan Nural Jawahir Hj Wan Yussof, “Content-based Image Retrieval using SIFT for Binary and Greyscale Images”, Proceedings of IEEE International Conference on Signal and Image Processing Applications, pp. 83-88, 2013. DOI: https://doi.org/10.1109/ICSIPA.2013.6707982
Yusuke Matsui, Kiyoharu Aizawa and Yushi Jing,“Sketch2manga:Sketch-basedMangaRetrieval”, Proceedings of IEEE International Conference on Image Processing, pp.3097-3101, 2014. DOI: https://doi.org/10.1109/ICIP.2014.7025626
Gholam Ali Montazer and Davar Giveki, “Content base Image Retrieval System using Clustered Scale Invariant feature Transforms”, Optik-International Journal for Light and Electron Optics, Vol. 126, No. 18, pp. 1695-1699, 2014 DOI: https://doi.org/10.1016/j.ijleo.2015.05.002
M.A.Z. Chahooki and N.M. Charkari, “Shape Retrieval Based on Manifold Learning by Fusion of Dissimilarity Measures”, IET Journal of Image Processing, Vol. 6, No. 4, 327-336, 2012. DOI: https://doi.org/10.1049/iet-ipr.2010.0548
Anuja Khodaskar and Siddarth Ladhake, “New-Fangled Alignment of Ontologies for Content Based Semantic Image Retrieval”, Proceedings of International Conference on Intelligent Computing, Communication and Convergence, Vol. 48, pp. 298-230, 2015. DOI: https://doi.org/10.1016/j.procs.2015.04.185
Nishant Shrivastava and Vipin Tyagi, “Content based Image Retrieval based on Relative Locations of Multiple Regions of Interest Using Selective Regions Matching”, Information Sciences, Vol. 259, pp. 212-224, 2014. DOI: https://doi.org/10.1016/j.ins.2013.08.043
K. Seetharaman and M. Kamarasan, “Statistical Framework for Image Retrieval based on Multiresolution Features and Similarity Method”, Multimedia Tools and Applications, Vol. 73, No. 3, pp. 1943-1962, 2014. DOI: https://doi.org/10.1007/s11042-013-1637-z
Mostafa Rahimi and Mohsen Ebrahimi Moghaddam, “A Content-Based Image Retrieval System Based on Color Ton Distribution Descriptors”, Signal, Image and Video Processing, Vol. 9, No. 3, pp. 691-704, 2015 DOI: https://doi.org/10.1007/s11760-013-0506-6
Lingyang Chu, Shuqiang Jiang, Shuhui Wang, Yanyan Zhang and Qingming Huang, “Robust Spatial Consistency Graph Model for Partial Duplicate Image Retrieval”, IEEE Transactions on Multimedia, Vol. 15, No. 8, pp. 1982-1996, 2013. DOI: https://doi.org/10.1109/TMM.2013.2270455
Sibendu Samanta, R.P. Maheshwari and Manoj Tripathy, “Directional Line Edge Binary Pattern for Texture Image Indexing and Retrieval”, Proceedings of International Conference on Advances in Computing, Communications and Informatics, pp. 746-750, 2012. DOI: https://doi.org/10.1145/2345396.2345518
Bin Xu, Jiajun Bu, Chun Chen, Can Wang, Deng Cai and Xiaofei He, “EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval”, IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 1, pp. 102-114, 2015. DOI: https://doi.org/10.1109/TKDE.2013.70
Daniel Carlos, Guimaraes Pedronette, Jurandy Almeida and Ricardo Da S. Torres, “A Scalable Re-Ranking Method for Content-based Image Retrieval”, Information Sciences, Vol. 265, pp. 91-104, 2014. DOI: https://doi.org/10.1016/j.ins.2013.12.030
Jun Yu, Dacheng Tao, Meng Wang and Yong Rui, “Learning to Rank using User Clicks and Visual Features for Image Retrieval”, IEEE Transactions on Cybernetics, Vol. 45, No. 4, pp. 767-779, 2015. DOI: https://doi.org/10.1109/TCYB.2014.2336697
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, pp. 583-594, 2015. DOI: https://doi.org/10.1109/TIP.2014.2378057
Eleftherios Tiakas, Dimitrios Rafailidis, Anastasios Dimou and Petros Daras, “MSIDX: Multi-Sort Indexing for Efficient Content-based Image Search and Retrieval”, IEEE Transactions on Multimedia, Vol. 15, No. 6, pp. 1415-1430, 2013 DOI: https://doi.org/10.1109/TMM.2013.2247989
Zhongmiao Xiao and Xiaojun Qi, "Complementary RelevanceFeedback-BasedContent-base damage Retrieval", Multimedia Tools and Applications, Vol. 73, No. 3, pp. 2157-2177, 2014. DOI: https://doi.org/10.1007/s11042-013-1693-4
Laxmi, Malay Nema and Subrata Rakshit, "Long Term Relevance Feedback: A Probabilistic Axis Re-Weighting Update Scheme", IEEE Signal Processing Letters, Vol. 22, No. 7, pp. 852-856, 2015.
Malay Kumar Kundu, Manish Chowdhury, and Samuel Rota Bulo, "A Graph-Based Relevance Feedback Mechanism in Content-Based Image Retrieval", Knowledge-based Systems, Vol. 73, pp. 254-264, 2015. DOI: https://doi.org/10.1016/j.knosys.2014.10.009
Aun Irtaza, M. Arfan Jaffar and Mannan Saeed Muhammad, "Content-Based Image Retrieval in a Web 3.0 Environment", Multimedia Tools Application, Vol. 74, No. 14, pp. 5055-5072, 2015. DOI: https://doi.org/10.1007/s11042-013-1679-2
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Devshri Satyarthi, Manish Dixit
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
