Review on CBIR Trends and Techniques to Upgrade Image Retrieval
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Multimedia is an ever growing field which has rich set of digital images. Earlier, document retrieval and Information Search was purely based on text. The problems faced by text based retrieval for image search have been overcome by CBIR (Content Based Image Retrieval) and the solutions were given by many researchers in various ways. The survey projected here paves a platform to understand how the images are processed by various CBIR systems and the performance obtained by those systems. The purpose of this review is to focus on the methodologies and approaches instigated for CBIR system and provide appropriate guidance for effective image retrieval. From the performance obtained by the existing CBIR systems, performance issues have been discussed for future research on images.
Copyright © 2014 Praise Worthy Prize - All rights reserved.
M. A. Gavrielides, E. Sikudova, and I. Pitas, “Color-based descriptors for image fingerprinting,” Multimedia, IEEE Trans., vol. 8, no. 4, pp. 740–748, 2006.
E. Aptoula and S. Lefèvre, “Morphological description of color images for content-based image retrieval,” Image Process. IEEE Trans., vol. 18, no. 11, pp. 2505–2517, 2009.
P. S. Suhasini, K. Krishna, and M. KRISHNA IV, “CBIR USING COLOR HISTOGRAM PROCESSING.,” J. Theor. Appl. Inf. Technol., vol. 6, no. 1, 2009.
A. B. Kurhe, S. S. Satonka, and P. B. Khanale, “Color matching of images by using minkowski-form distance,” Glob. J. Comput. Sci. Technol., vol. 11, no. 5, 2011.
J. Stottinger, A. Hanbury, N. Sebe, and T. Gevers, “Sparse color interest points for image retrieval and object categorization,” Image Process. IEEE Trans., vol. 21, no. 5, pp. 2681–2692, 2012.
P. Kaur, S. Thakral, and M. Singh, “Color Based Image Retrieval System,” IOSR J. Comput. Eng., vol. 1, no. 5, pp. 1–5, 2012.
M. S. P. Pant, “Content Based Image Retrieval Using Color Feature,” Int. J. Eng., vol. 2, no. 4, 2013.
D. C. Guimarães Pedronette, J. Almeida, and R. Da S Torres, “A scalable re-ranking method for content-based image retrieval,” Inf. Sci. (Ny)., vol. 265, pp. 91–104, 2014.
C.-H. Lin, C.-C. Chen, H.-L. Lee, and J.-R. Liao, “Fast K-means algorithm based on a level histogram for image retrieval,” Expert Syst. Appl., vol. 41, no. 7, pp. 3276–3283, 2014.
I. Bartolini, P. Ciaccia, and M. Patella, “Warp: Accurate retrieval of shapes using phase of fourier descriptors and time warping distance,” Pattern Anal. Mach. Intell. IEEE Trans., vol. 27, no. 1, pp. 142–147, 2005.
I. Kolonias, D. Tzovaras, S. Malassiotis, and M. G. Strintzis, “Fast content-based search of VRML models based on shape descriptors,” Multimedia, IEEE Trans., vol. 7, no. 1, pp. 114–126, 2005.
A. Bishnu, B. B. Bhattacharya, M. K. Kundu, C. A. Murthy, and T. Acharya, “Euler vector for search and retrieval of gray-tone images,” Syst. Man, Cybern. Part B Cybern. IEEE Trans., vol. 35, no. 4, pp. 801–812, 2005.
D. Zhang and G. Lu, “Shape-based image retrieval using generic Fourier descriptor,” Signal Process. Image Commun., vol. 17, no. 10, pp. 825–848, 2002.
S. Kiranyaz, M. Ferreira, and M. Gabbouj, “A generic shape/texture descriptor over multiscale edge field: 2-D walking ant histogram,” Image Process. IEEE Trans., vol. 17, no. 3, pp. 377–391, 2008.
S. Li, M.-C. Lee, and C.-M. Pun, “Complex Zernike moments features for shape-based image retrieval,” Syst. Man Cybern. Part A Syst. Humans, IEEE Trans., vol. 39, no. 1, pp. 227–237, 2009.
M. Sahu and M. A. Rizvi, “Optimized Image Retrieval System: Texture and Shape Based Approach,” Int. J. Comput. Appl., vol. 4, no. 3, 2013.
Y. Avrithis and G. Tolias, “Hough Pyramid Matching: Speeded-Up Geometry Re-ranking for Large Scale Image Retrieval,” Int. J. Comput. Vis., vol. 107, no. 1, pp. 1–19, 2014.
S.-M. Lee, H.-J. Bae, and S.-H. Jung, “Efficient content-based image retrieval methods using color and texture,” ETRI J., vol. 20, no. 3, pp. 272–283, 1998.
Y. D. Chun, N. C. Kim, and I. H. Jang, “Content-based image retrieval using multiresolution color and texture features,” Multimedia, IEEE Trans., vol. 10, no. 6, pp. 1073–1084, 2008.
J. Yue, Z. Li, L. Liu, and Z. Fu, “Content-based image retrieval using color and texture fused features,” Math. Comput. Model., vol. 54, no. 3, pp. 1121–1127, 2011.
X.-Y. Wang, B.-B. Zhang, and H.-Y. Yang, “Content-based image retrieval by integrating color and texture features,” Multimed. Tools Appl., vol. 68, no. 3, pp. 545–569, 2014.
M. Singha and K. Hemachandran, “Content based image retrieval using color and texture,” Signal Image Process. An Int. J. Vol, vol. 3, pp. 39–57, 2012.
S. M. Singh and K. Hemachandran, “Content-Based Image Retrieval using Color Moment and Gabor Texture Feature.,” Int. J. Comput. Sci. Issues, vol. 9, no. 5, 2012.
M. M. V Lande, “Prof. Praveen Bhanodiya, Mr. Pritesh Jain,Efficient Content Based Image Retrieval Using Color and Texture,” Int. J. Sci. Eng. Res., vol. 4, no. 6, p. 121, 2013.
E. R. Vimina and K. P. Jacob, “Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest,” J. Image Graph., vol. 1, no. 1, 2013.
J. Prabhu and J. S. Kumar, “WAVELET BASED CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL COOCURENCE MATRIX AND COLOR COOCURENCE MATRIX,” J. Comput. Sci., vol. 10, no. 1, p. 15, 2013.
M. E. ElAlami, “A new matching strategy for content based image retrieval system,” Appl. Soft Comput., vol. 14, pp. 407–418, 2014.
N. Shrivastava and V. Tyagi, “Content based image retrieval based on relative locations of multiple regions of interest using selective regions matching,” Inf. Sci. (Ny)., vol. 259, pp. 212–224, 2014.
S. Murala and Q. M. Jonathan Wu, “MRI and CT image indexing and retrieval using local mesh peak valley edge patterns,” Signal Process. Image Commun., vol. 29, no. 3, pp. 400–409, 2014.
H.-H. Tsai, B.-M. Chang, and S.-H. Liou, “Rotation-invariant texture image retrieval using particle swarm optimization and support vector regression,” Appl. Soft Comput., vol. 17, pp. 127–139, 2014.
R. S. Dubey, R. Choubey, and J. Bhattacharjee, “Multi Feature Content Based Image Retrieval.,” Int. J. Comput. Sci. Eng., vol. 2, no. 6, 2010.
O. Bencharef, B. Jarmouni, and A. So, “Research of Similar Images Based on Global Descriptors and Multiple Clustering.,” Int. J. Eng. Technol., vol. 5, no. 3, 2013.
M. Babu Rao, B. Prabhakara Rao, and A. Govardhan, “CONTENT BASED IMAGE RETRIEVAL USING DOMINANT COLOR, TEXTURE AND SHAPE.,” Int. J. Eng. Sci. Technol., vol. 3, no. 4, 2011.
A. Nigam, A. K. Garg, and R. C. Tripathi, “Content based Trademark Retrieval by Integrating Shape with Colour and Texture Information.,” Int. J. Comput. Appl., vol. 22, 2011.
S. M. Patil, “Content Based Image Retrieval Using Color, Texture & Shape,” Int. J. Comput. Sci. Eng. Technol., vol. 3, no. 9, 2012.
E. R. Vimina and K. P. Jacob, “CBIR Using Local and Global Properties of Image Sub-blocks,” Int. J. Adv. Sci. Technol., vol. 48, 2012.
S. H. Jadhav and S. A. Ahmed, “A Content Based Image Retrieval System using homogeneity Feature extraction from Recency-based Retrieved Image Library,” IOSR J. Comput. Eng., vol. 7, no. 6, 2012.
D. K. Iakovidis, N. Pelekis, E. E. Kotsifakos, I. Kopanakis, H. Karanikas, and Y. Theodoridis, “A pattern similarity scheme for medical image retrieval,” Inf. Technol. Biomed. IEEE Trans., vol. 13, no. 4, pp. 442–450, 2009.
M. V. Dass, M. M. Ali, and M. R. Ali, “Image Retrieval Using Interactive Genetic Algorithm,” in Computational Science and Computational Intelligence (CSCI), 2014 International Conference on, 2014, vol. 1, pp. 215–220.
Y. D. Khan, F. Ahmad, and S. A. Khan, “Content-based image retrieval using extroverted semantics: a probabilistic approach,” Neural Comput. Appl., vol. 24, no. 7–8, pp. 1735–1748, 2014.
X.-Y. Wang, Y.-W. Li, H.-Y. Yang, and J.-W. Chen, “An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification,” Neurocomputing, vol. 127, pp. 214–230, 2014.
S. Zhang, Q. Tian, G. Hua, Q. Huang, and W. Gao, “ObjectPatchNet: Towards scalable and semantic image annotation and retrieval,” Comput. Vis. Image Underst., vol. 118, pp. 16–29, 2014.
P. Maheshwary and N. Sricastava, “Prototype System for Retrieval of Remote Sensing Images based on Color Moment and Gray Level Co-Occurrence Matrix.,” Int. J. Comput. Sci. Issues, vol. 7, no. 4, 2010.
C. P. Malar and S. Vellaisamy, “A Novel Feature Extraction for Texture Processing,” Int. J. Comput. Sci. Issues, vol. 5, no. 1, pp. 8–13, 2012.
M. Arebey, M. A. Hannan, R. A. Begum, and H. Basri, “CBIR for an automated solid waste bin level detection system using GLCM,” in Visual Informatics: Sustaining Research and Innovations, Springer, 2011, pp. 280–288.
V. Sebastian, A. Unnikrishnan, K. Balakrishnan, and others, “GREY LEVEL CO-OCCURRENCE MATRICES: GENERALISATION AND SOME NEW FEATURES.,” Int. J. Comput. Sci. Eng. Inf. Technol., vol. 2, no. 2, 2012.
K. N. Reddy, P. P. Kumari, and others, “Sketch Based Image Retrieval Approach Using Gray Level Co-Occurrence Matrix,” Int. J. Sci. Eng. Appl., vol. 1, no. 1, pp. 67–71, 2012.
S. Sulochana and R. Vidhya, “Texture Based Image Retrieval Using Framelet Transform--Gray Level Co-occurrence Matrix (GLCM),” Int. J. Adv. Res. Artif. Intell., vol. 2, no. 2, 2013.
P. Mohanaiah, P. Sathyanarayana, and L. GuruKumar, “Image Texture Feature Extraction Using GLCM Approach,” Int. J. Sci. Res. Publ., vol. 3, no. 5, p. 1, 2013.
D.-H. Kim, J.-W. Song, J.-H. Lee, and B.-G. Choi, “Support vector machine learning for region-based image retrieval with relevance feedback,” ETRI J., vol. 29, no. 5, pp. 700–702, 2007.
A. Marakakis, N. Galatsanos, A. Likas, and A. Stafylopatis, “Relevance Feedback for Content-Based Image Retrieval Using Support Vector Machines and Feature Selection,” in Artificial Neural Networks--ICANN 2009, Springer, 2009, pp. 942–951.
C. Rao, S. S. Kumar, B. C. Mohan, and others, “CONTENT BASED IMAGE RETRIEVAL USING EXACT LEGENDRE MOMENTS AND SUPPORT VECTOR MACHINE.,” Int. J. Multimed. Its Appl., vol. 2, no. 2, 2010.
M. M. Rahman, S. K. Antani, and G. R. Thoma, “A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback,” Inf. Technol. Biomed. IEEE Trans., vol. 15, no. 4, pp. 640–646, 2011.
A. Quddus and O. Basir, “Semantic image retrieval in magnetic resonance brain volumes,” Inf. Technol. Biomed. IEEE Trans., vol. 16, no. 3, pp. 348–355, 2012.
L. Zhang, L. Wang, and W. Lin, “Semisupervised biased maximum margin analysis for interactive image retrieval,” Image Process. IEEE Trans., vol. 21, no. 4, pp. 2294–2308, 2012.
R. R. Eamani and G. V Hari Prasad, “Content-Based Image Retrieval Using Support Vector Machine in digital image processing techniques.,” Int. J. Eng. Sci. Technol., vol. 4, no. 4, 2012.
K. A. Kumar and Y. V. B. Reddy, “Content Based Image Retrieval Using SVM Algorithm,” Int. J. Electr. Electron. Eng. (IJEEE), ISSN, vol. 1, no. 3, 2012.
S. Jain, “A Machine Learning Approach: SVM for Image Classification in CBIR,” Int. J. Appl. or Innov. Eng. Manag., vol. 2, no. 4, pp. 446–452, 2013.
X. He, O. King, W.-Y. Ma, M. Li, and H.-J. Zhang, “Learning a semantic space from user’s relevance feedback for image retrieval,” Circuits Syst. Video Technol. IEEE Trans., vol. 13, no. 1, pp. 39–48, 2003.
J.-H. Su, W.-J. Huang, P. S. Yu, and V. S. Tseng, “Efficient relevance feedback for content-based image retrieval by mining user navigation patterns,” Knowl. Data Eng. IEEE Trans., vol. 23, no. 3, pp. 360–372, 2011.
R. R. Yager and F. E. Petry, “A framework for linguistic relevance feedback in content-based image retrieval using fuzzy logic,” Inf. Sci. (Ny)., vol. 173, no. 4, pp. 337–352, 2005.
W. Xiaoling and X. Kanglin, “Application of the fuzzy logic in content-based image retrieval,” J. Comput. Sci. Technol., vol. 5, pp. 19–24, 2005.
M. Ceccarelli, F. Musacchia, and A. Petrosino, “Content-based image retrieval by a fuzzy scale-space approach,” Int. J. Pattern Recognit. Artif. Intell., vol. 20, no. 06, pp. 849–867, 2006.
H. A. Ahmed, N. El Gayar, and H. Onsi, “A New Approach in Content-Based Image Retrieval Using Fuzzy Logic,” INFOS, pp. 34–41, 2008.
M. Banerjee, M. K. Kundu, and P. Maji, “Content-based image retrieval using visually significant point features,” Fuzzy Sets Syst., vol. 160, no. 23, pp. 3323–3341, 2009.
V. V Balamurugan and P. Anandhakumar, “Neuro-Fuzzy Based Clustering Approach For Content Based Image Retrieval Using 2DWavelet Transform.,” Int. J. Signal Image Process., vol. 1, no. 1, 2010.
J. M. Medina, S. Jaime-Castillo, C. D. Barranco, and J. R. Campana, “On the Use of a Fuzzy Object-Relational Database for Flexible Retrieval of Medical Images,” Fuzzy Syst. IEEE Trans., vol. 20, no. 4, pp. 786–803, 2012.
A. M. Humadi and H. A. Younis, “Application of the Fuzzy Logic in Content Based Image Retrieval using Color Feature,” Int. J. Comput. Sci. Mob. Comput., vol. 3, no. 2, pp. 170–180, 2014.
J. Laaksonen, M. Koskela, and E. Oja, “PicSOM-self-organizing image retrieval with MPEG-7 content descriptors,” Neural Networks, IEEE Trans., vol. 13, no. 4, pp. 841–853, 2002.
N. S. Kojić, S. K. Čabarkapa, G. J. Zajić, and B. D. Reljin, “Implementation of neural network in CBIR systems with relevance feedback,” J. Autom. Control, vol. 16, no. 1, pp. 41–45, 2006.
S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “IMAGE RETRIEVAL USING CUBIC SPLINES NEURAL NETWORKS.,” Int. J. Video Image Process. Netw. Secur., vol. 9, no. 10, 2009.
B. Jyothi and U. Shanker, “Neural network approach for image retrieval based on preference elicitation,” Int. J. Comput. Sci. Eng., vol. 2, no. 4, pp. 934–941, 2010.
C. Durai and V. Duraisamy, “Content Based Image Retrieval using Novel Gaussian Fuzzy Feed Forward-Neural Network.,” J. Comput. Sci., vol. 7, no. 7, 2011.
M. Divya, J. Janet, and R. Suguna, “A genetic optimized neural network for image retrieval in telemedicine,” EURASIP J. Image Video Process., vol. 2014, no. 1, pp. 1–9, 2014.
M. A. N. Al-Azawi, “Neural Network Based Automatic Traffic Signs Recognition,” Int. J. Digit. Inf. Wirel. Commun., vol. 1, no. 4, pp. 753–766, 2011.
P. S. Patheja, A. A. Waoo, and J. P. Maurya, “CBIR BASED ON LEARNING OF NEURAL NETWORK WITH FEEDBACK RELEVANCE,” Int. J. Res. Eng. Appl. Sci., vol. 2, no. 2, pp. 1360–1370, 2012.
A. Nagathan and M. I. Manimozhi, “Content-Based Image Retrieval System Using Feed-Forward Backpropagation Neural Network,” Int. J. Comput. Sci. Eng., vol. 2, no. 4, pp. 143–151, 2013.
Y. D. Chun, S. Y. Seo, and N. C. Kim, “Image retrieval using BDIP and BVLC moments,” Circuits Syst. Video Technol. IEEE Trans., vol. 13, no. 9, pp. 951–957, 2003.
G. Quellec, M. Lamard, G. Cazuguel, B. Cochener, and C. Roux, “Adaptive nonseparable wavelet transform via lifting and its application to content-based image retrieval,” Image Process. IEEE Trans., vol. 19, no. 1, pp. 25–35, 2010.
Y. Yang and S. Newsam, “Geographic image retrieval using local invariant features,” Geosci. Remote Sensing, IEEE Trans., vol. 51, no. 2, pp. 818–832, 2013.
S. Murala, R. P. Maheshwari, and R. Balasubramanian, “Local tetra patterns: a new feature descriptor for content-based image retrieval,” Image Process. IEEE Trans., vol. 21, no. 5, pp. 2874–2886, 2012.
E. Aptoula, “Remote Sensing Image Retrieval With Global Morphological Texture Descriptors,” Geosci. Remote Sensing, IEEE Trans., vol. 52, no. 5, pp. 3023–3034, 2014.
L. Chu, S. Jiang, S. Wang, Y. Zhang, and Q. Huang, “Robust Spatial Consistency Graph Model for Partial Duplicate Image Retrieval,” Multimedia, IEEE Trans., vol. 15, no. 8, pp. 1982–1996, 2013.
E. Tiakas, D. Rafailidis, A. Dimou, and P. Daras, “MSIDX: Multi-Sort Indexing for Efficient Content-Based Image Search and Retrieval,” IEEE Trans. Multimed., vol. 15, no. 6, pp. 1415–1430, 2013.
D. Espinoza-Molina and M. Datcu, “Earth-Observation Image Retrieval Based on Content, Semantics, and Metadata,” Geosci. Remote Sensing, IEEE Trans., vol. 51, no. 11, pp. 5145–5159, 2013.
M. Jian and K.-M. Lam, “Face-image retrieval based on singular values and potential-field representation,” Signal Processing, vol. 100, pp. 9–15, 2014.
J. Liao, D. Yang, T. Li, J. Wang, Q. Qi, and X. Zhu, “A scalable approach for content based image retrieval in cloud datacenter,” Inf. Syst. Front., vol. 16, no. 1, pp. 129–141, 2014.
S. Zhu, L. Zou, and B. Fang, “Content based image retrieval via a transductive model,” J. Intell. Inf. Syst., vol. 42, no. 1, pp. 95–109, 2014.
S. A. Chatzichristofis, C. Iakovidou, Y. S. Boutalis, and E. Angelopoulou, “Mean Normalized Retrieval Order (MNRO): a new content-based image retrieval performance measure,” Multimed. Tools Appl., vol. 70, no. 3, pp. 1767–1798, 2014.
C. Beecks, S. Kirchhoff, and T. Seidl, “On stability of signature-based similarity measures for content-based image retrieval,” Multimed. Tools Appl., vol. 71, no. 1, pp. 349–362, 2014.
T. Osman, D. Thakker, and G. Schaefer, “Utilising semantic technologies for intelligent indexing and retrieval of digital images,” Computing, vol. 96, no. 7, pp. 651–668, 2014.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2019 Praise Worthy Prize