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Textual and Shape based Features with Indexing Mechanisms for Image Retrieval: A Survey

P. Sumathy, A. Vadivel


The technological advancements of Internet have increased the population of images, which demands effective retrieval mechanisms. Search engines have become indispensable tools for retrieving relevant images from WWW. Therefore it becomes necessary for retrieval systems that retrieve documents with images. In this paper, we present the comprehensive review on the state-of-the-art literature on text and shape based feature extraction methods and retrieval schemes. The literature review has brought out the research issues in both Text Based Image Retrieval and Content Based Image Retrieval schemes. These issues and problems associated with the existing TBIR and CBIR techniques has been  identified through literature review, which laid the foundation of many image retrieval techniques.


CBIR, Indexing Schemes, TBIR, Textual Keyword.

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Ziyang Liu and Yi Chen, “Identifying return information for XML keyword search”, in Proceedings of ACM International Conference on Management of Data, (SIGMOD’07), Beijing, ACM, 2007, pp. 329-340.

W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, P. Yanker, C. Faloutsos and G. Taubin, “The QBIC project: Querying images by content using colour, texture and shape”, SPIE International Conference of Society of Optical Engineering in Storage and Retrieval for Image and Video Database, San Jose, Vol. 1908, 1993, pp. 173-187.

A. Pentland, R. W. Picard and S. Sclaroff, “Photobook: Tools for content-based manipulation of image databases”, Multimedia Tools and Applications, The Kluwer International Series in Engineering and Computer Science, Vol. 359, 1996, pp. 43-80.

J. R. Smith and S. F. Chang, “Visual SEEK: A fully automated content-based image query system”, in Proceedings of the Forth ACM International Conference on Multimedia (ACM MM’96), Boston, ACM, 1996, pp. 87–98.

J. Z. Wang, J. Li and G. Wiederhold, “SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries”, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 23, No. 9, 2000, pp. 947-963.

C. Carson, S. Belongie, H. Greenspan and J. Malik, “Blobworld: Image Segmentation using Expectation–Maximization and its application to Image Querying”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol, 24, No. 8, 2002, pp. 1026–1038.

A. Gupta and R. Jain, “Visual Information Retrieval”, Internal Journal of Communication of ACM, Vol. 40, No. 5, 1997, pp. 70–79.

W. Y. Ma and B. Manjunath, “Netra: A toolbox for navigating large image databases”, in Proceedings of International Conference on Image Processing (ICIP 1997), Washington, 1997, pp. 568–571.

Y. Rui, T. S. Huang and S. F. Chang, “Image retrieval: current techniques, promising directions and open issues”, Journal of Visual Communication and Image Representation, Vol. 10, No. 4, 1999, pp. 39-62.

F. Long, H. J. Zhang and D.D. Feng, “Fundamentals of content-based image retrieval”, Multimedia Information Retrieval and Management Signals and Communication Technology”, Springer, 2003, pp. 1-26.

T. Gevers and H. M. G. Stokman, “Robust Histogram Construction from Color Invariants for Object Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No. 1, 2004, pp. 113-118.

Yining Deng, B. S. Manjunath, C. Kenney, M. S. Moore and H. Shin, “An efficient colour representation for image retrieval”, IEEE Transactions on Image Processing, Vol. 10, No. 1, 2001, pp. 140-147.

A. Vadivel, ShamikSural and A. K. Majumdar, “Robust Histogram Generation from the HSV Color Space based on Visual Perception”, International Journal on Signals and Imaging Systems Engineering , Inderscience, Vol. 1, No.3/4, 2008, pp. 245-254.

A. Vadivel, ShamikSural and A. K. Majumdar, “An Integrated Color and Intensity Co-Occurrence Matrix”, Pattern Recognition Letters, Elsevier Science, Vol. 28, No. 8, 2007, pp. 974-983.

C. Palm, “Color Texture Classification by Integrative Co-Occurrence Matrices”, Pattern Recognition, Elsevier Science, Vol. 37, No.5, 2004, pp. 965-976.

Ying Liu, Dengsheng Zhang and Guojun Lu, “Region-based image retrieval with high-level semantics using decision tree learning”, Pattern Recognition, Elsevier, Vol. 41, No. 8, 2008, pp. 2554-2570.

H. Feng, R. Shi and T. S. Chua, “A bootstrapping framework for annotating and retrieving WWW images”, in Proceedings of 12th Annual ACM International Conference on Multimedia (MULTIMEDIA’04), New York, USA, ACM, 2004, pp. 960-967.

H. M. Sanderson and M. D. Dunlop, “Image retrieval by hypertext links”, in Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval, 1997, ACM, pp. 296-303.

H. T. Shen, B. C. Ooi and K. L. Tan, “Giving meaning to WWW images”, ACM Multimedia, LA, USA, 2000, pp. 39-47.

Jing Feng, Mingjing Li, Hong-Jiang Zhang and Bo Zhang, “A Unified Framework for Image Retrieval Using Keyword and Visual Features”, IEEE Transaction on Image Processing, Vol. 14, No. 7, 2005, pp. 979–989.

R. Zhao and W. I. Grosky, “Narrowing the Semantic Gap-Improved Text-Based Web Document Retrieval using Visual Features”, IEEE Transactions on Multimedia, Vol. 4, No. 2, 2002, pp. 189-200.

Deng Cai, Xiaofei He, Wei -Ying Ma, Ji-Rong Wen, and H. Zhang, “Organizing WWW Images based on the Analysis of Page Layout and Web Link Structure”, in Proceedings of IEEE International Conference on Multimedia Expo (ICME’04), Taiwan, IEEE, 2004, pp. 113-116.

Deng Cai, S. Yu, Ji - Rong Wen and Wei - Ying Ma, “VIPs a Vision based Page Segmentation Algorithm”, Microsoft Technical Report, MSR-TR-2003-79, 2003.

Chen Wu and Xiaohua Hu, “Applications of Rough set decompositions in Information Retrieval”, International Journal of Electrical and Electronics Engineering, Vol. 4, No. 4, 2010, pp. 285-290.

S. Tollari, H. Glotin, J. Le, and J. L. Maitre, “Enhancement of Textual Images Classification using Segmented Visual Contents for Image Search Engine”. Multimedia Tools and Applications, Vol. 25, No. 3, 2005, pp. 405–417.

J. Han, K. N. Ngan, M. Li and H. J. Zhang, “A Memory Learning Framework for Effective Image Retrieval”, IEEE Transactions on Image Processing, Vol. 14, No. 4, 2005, pp. 511–524.

F. Xu and Y-J. Zhang, “Integrated patch model: A Generative Model for Image Categorization based on Feature Selection”, Pattern Recognition Letters, Elsevier Science, Vol. 28, No. 12, 2007, pp. 1581–1591.

P.Shanmugavadivu, P.Sumathy, A.Vadivel, “Ranking images in Web Documents based on HTML TAGs for image retrieval from WWW”, International Journal of Computational Intelligence Studies, Inderscience Publishers, Vol No. 3, 2014.

W. Hu, O. Wu, Z. Chen, Z. Fu and S. Maybank, “Recognition of Pornographic Web Pages by Classifying Texts and Images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 6, 2007, pp. 1019–1034.

A. Vadivel, ShamikSural and A. K. Majumdar, “Image Retrieval from Web using Multiple Features Online Information Review”, Emerald, Vol. 33, No. 6, 2009, pp. 1169–1188.

DenizKiling and AdilAlpkocak, “An expansion and reranking approach for annotation-based image retrieval from Web”, International Journal of Expert Systems with Applications, Vol. 38, No. 10, 2011, pp. 13121-13127.

Y. A Aslandogan and C.T Yu, “Techniques and Systems for Image and Video Retrieval”, IEEE Transaction on Knowledge and Data Engineering, Vol. 11, No. 1, 1999, pp. 56 –63.

E. A. El Kwae and M. R. Kabuka, “Efficient Content-Based Indexing of Large Image Databases”, ACM Transaction on Information Systems, Vol. 18, No. 2, 2000, pp. 171-210.

J. Cox Ingemar, M. L. Miller, T. P. Minka, T. Papathomas and P. N. Yianilos, “The Bayesian Image Retrieval System, PicHunter: Theory, implementation and Psychophysical Experiments”, IEEE Transactions on Image Processing, Vol. 9, No. 1, 2000, pp. 3-19.

Zheng Chen, Liu Wenyin, Feng Zhang, Mingjing Li and HongjiangZang, “Web Mining for Web Image Retrieval”, Journal on American Society of Information Science and Technology, Vol. 52, No. 10, 2001, pp. 831-839.

C. Hu, X. Zh, H. Zhang and Q. Yang, “A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems”, in Proceedings in Eighth ACM International Conference in Multimedia (MULTIMEDIA’ 00), Los Angels, ACM, 2000, pp. 31-37.

Y. M. Y. Hasan and L. J. Karam, “Morphological text extraction from images”, IEEE Transactions on Image Processing, Vol. 9, No. 11, 2000, pp. 1978–1983.

Yen Lin Chen and Bing Fie Wu, “A multi-plane segmentation approach for text extraction from complex document images”, Pattern Recognition, Vol. 42, No. 7, 2009, pp. 1419–1444.

Y. L. Chen, Z. W. Hong and C. H. Chuang, “A knowledge-based system for extracting text-lines from mixed and overlapping text/graphics compound document images”, International Journal of Expert Systems with Applications, Vol. 39, 2012, pp. 494–507.

K. Yanai, “Generic image classification using visual knowledge on the web”, in Proceedings of ACM Multimedia”, Berkeley, USA, ACM, 2003, pp. 167-176.

Kobus Barnard and David Forsyth, “Learning the Semantics of Words and Pictures”, in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (ICCV’01), Vancouver, Vol. 2, pp. 408–415,IEEE, 2001, July 07-14.

Hsin-Chang Yang and Chung-Hong Lee, “Image semantics discovery from web pages for semantic-based image retrieval using self-organizing maps”, International Journal on Expert Systems with Applications, Vol. 34, No. 1, 2006, pp. 266-279.

Fuxiang Lu, Xiaokang Yang, Rui Zhang, and Songyu Yu ,“Image classification based on pyramid histogram of topics”, in Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2009), New York, USA, IEEE, 2009, pp. 398-401.

Anil K. Jain and AdityaVailaya, “Image retrieval using colour and shape”, Pattern Recognition, Elsevier, Vol. 29, No. 8, 1996, pp. 1233-1244.

J. R. Kender, “Saturation, Hue and NormalisedColour: Calculation, Digitisation and Use”, Computer Science Technical Report, Carnegie-Mellon University, Pittsburg, USA, 1976.

Zhang Lei, Lin Fuzong and Zhang Bo, “A CBIR method based colour-spatial feature”, in Proceedings of IEEE Region 10th Annual International Conference (TENCON 99), Cheju Island, South Korea, IEEE, 1999, pp. 166-169.

A. Mohamed, F. Khellfi, Y. Weng, J. Jiang and S. Ipson, “An efficient Image Retrieval through DCT Histogram Quantization", in Proceedings of International Conference on Cyber Worlds, 2009, pp. 237-240.

H. Nezamabadi-pour and E. Kabir, “Image retrieval using histograms of uni-colour and bi-colour blocks and directional changes in intensity gradient”, Pattern Recognition Letters, Vol. 25, No. 14, 2004, pp. 1547-1557.

M. J. Swain and D. Ballard, “Colour Indexing”, Computer Vision, Vol. 7, 1991, pp. 11-32.

Shoujue Wang and Hong Qin, “A Study of Order-Based Block Colour Feature Image Retrieval Compared with Cumulative Colour Histogram Method”, in Proceedings of Sixth International Conference on Fuzzy Systems and Knowledge Discovery, Vol. 1, 2009, pp. 81-84.

Wang Xiaoling, “A Novel Circular Ring Histogram for Content-Based Image Retrieval”, in Proceedings of First International Workshop on Education Technology and Computer Science, Vol. 2, 2009, pp. 785-788.

Y. Gong, G. Proietti and C. Faloutsos, “Image indexing and retrieval based on human perceptual color clustering”, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition,IEEE, 1998, pp.578-583.

A. T. Zahn and R. Z. Roskies, “Fourier Descriptors for Plane Closed Curves”, IEEE Transactions on Computers, Vol. 2, No. 3, 1972, pp. 269-281.

P. Zhang, B. Verma and K. Kumar, “Neural Vs. statistical classifier in conjunction with genetic algorithm based feature selection”, Pattern Recognition, Elsevier, Vol. 26, 2005, pp. 909-919.

De Cao Tran, Kinji Ono, “Content-Based Image Retrieval: Object Representation by the Density of Feature Points”, National Institute of Informatics, Tokyo, 2003.

G. Lu and A. Sajjanhar, “Region-based Shape representation and Similarity Measure Suitable for Content-based Image Retrieval”, Multimedia Systems, Springer-Verlag, Vol. 7, No. 2, 1999, pp. 165-174.

Y. Rui, A. C. She and T. S. Huang, “A Modified Fourier Descriptor for Shape Matching in MARS, in Proceedings of Image Databases and Multimedia Search, Series of Software Engineering and Knowledge Engineering, World Scientific Publishing, Singapore, Vol.8, 1998, pp.165-180.

V. N. Gudivada and V. V. Raghavan, “Special Issue on Content-Based Image Retrieval Systems”, IEEE Computer, Vol. 28, No. 9, 1995, pp. 18-22.

Y. Tao and W. I. Grosky, “Delaunay Triangulation for Image Object Indexing: A Novel Method for Shape Representation”, in Proceedings of IS&T/SPIE's Symposium on Storage and Retrieval for Image and Video Databases, San Jose, California, 1999.

H. Freeman and A. Saghri, “Generalized chain codes for planar curves”, in Proceedings of the 4th International Joint Conference on Pattern Recognition, Kyoto, Japan, 1978, pp. 701-703.

E. M. Arkin, L. Chew, D. Huttenlocher, K. Kedem and J. Mitchell, “An Efficiently Computable Metric for Comparing Polygonal Shapes”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 3, 1991, pp. 209-216.

M. Safar, C. Shababi and X. Sun, “Image Retrieval by Shape: A comparative Study”, in Proceedings of IEEE International Conference on Multimedia and Expo, Vol. 1,IEEE, 2000, pp. 141-144.

H. C. Yang and C. H. Lee, “Image Semantics Discovery from Web Pages for Semantic-based Image Retrieval using Self-organizing maps”, Expert Systems with Applications: An Integrated Journal, Vol. 34, No. 1, 2008, pp. 266– 279.

F. Mahmoudi, J. Shanbehzadeh, A. M. EftekhariMoghadam and H. SoltanianZadeh, “Image retrieval based on shape similarity by edge orientation auto correlogram”, Pattern Recognition, Elsevier Science, Vol. 36, No. 8, 2003, pp. 1725-1736.

Z. Hou and T. S. Koh, “Robust edge detection”, Pattern Recognition, Elsevier Science, Vol. 36, No. 9, 2003, pp. 2083-2091.

N. Xing and I.S. Ahmad, “Shape-Based Image Retrieval”, in Proceedings of 7th International Conference on Advances in Mobile Computing and Multimedia, December 14-16, 2009, pp. 545-549.

A. Oliva and A. Torralba, “Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope”, International Journal of Computer Vision, Vol. 42, No. 3, 2001, pp. 145–175.

K. Grauman and B. Leibe, “Indexing and visual vocabularies, Expert chapter from Synthesis lecture draft: Visual recognition”, IES, Chapter 5, 2009, pp. 62-69.

C. F. Joaquim, X. R. Marcela, P. M. Elaine, J. M. Agma and T. J. Caetano, “A Low-cost Approach for Effective Shape-based Retrieval and Classification of Medical Images”, in Proceedings of Seventh IEEE International Symposium on Multimedia (ISM’05),IEEE, 2005, pp. 565-570.

K. Zagoris, K. Ergina and N. Papamarkos, “Image retrieval systems based on compact shape descriptor and relevance feedback information”, Journal of Visual Communication and Image Representation, Elsevier Science, Vol. 22, No. 5, 2011, pp. 378-390.

M. A. Z. Chahooki and N. M. Charkari, “Supervised Shape Retrieval based on Fusion of Multiple Feature Spaces”, in Proceedings of 20th Iranian Conference on Electrical Engineering (ICEE’12), Tehran, Iran, 2012, pp. 1072-1074.

PatrizioFrosini and Claudia Landi, “Persistent Betti numbers for a noise tolerant shape based approach to image retrieval”, Pattern Recognition Letter, Vol. 34, 2013, pp. 863-872.

S. Belongie and J. Malik, “Matching with Shape Contexts”, in Proceeding of IEEE Workshop on Content based Access of Image and Video Libraries, Hilton Head Island, 2000, pp. 20–26.

DimovDimo, “Fast Shape based Image Retrieval”, in Proceedings of Computer Systems and Technologies - CompSysTech: E-learning, 2003, pp. 296-302.

G. Mori, S. Belongie and J. Malik, “Shape contexts enable efficient retrieval of similar shapes”, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, IEEE, 2001 pp.723-730.

G. Mori, S. Belongie and J. Malik, “Efficient shape matching using shape context”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 11, 2005, pp. 1832–1837.

D. Zhang and G. Lu, “Shape-based image retrieval using generic Fourier descriptor”, Signal Processing: Image Communication, Vol. 17, No. 10, 2002, pp. 825–848.

P. Frosini, C. Landi, “Size theory as a topological tool for computer vision”, Pattern Recognition and Image Analysis, Vol. 9, No.4, 1999, pp. 596-603.

G. Muhammad, “Date fruits classification using texture descriptors and shape-size features”, Engineering Applications of Artificial Intelligence, Elsevier Science, Vol. 37, 2015, pp. 361–367.

D. Pickup, X. Sun, P. L. Rosin and R. R. Martin , “Euclidean-distance-based canonical forms for non-rigid 3D shape retrieval”, Pattern Recognition, Elsevier Science, Vol. 48, No. 8, 2015, pp. 2500-2512.

Z. Liu, C. Xie, S. Bu, X. Wang, J. Han, H. Lin and H. Zhang, “Indirect shape analysis for 3D shape retrieval”, Computers and Graphics, Vol. 46, 2015, pp. 110–116.

B. Wang, D. Brown, Y. Gao and J. L. Salle, “MARCH: Multiscale-arch-height description for mobile retrieval of leaf images”, Information Sciences, Elsevier Science, Vol. 302, 2015, pp. 132–148.

L. Quan and K. Tang, “Polynomial local shape descriptor on interest points for 3D part-in-whole matching”, Computer-Aided Design, Elsevier Science, Vol. 59, 2015, pp. 119–139.

ZhenzhongKuang, Zongim Li, Xiaxia Jiang, Yujie Liu and Hua Li, “Retrieval of non-rigid 3D shapes from multiple aspects”, Computer-Aided Design, Elsevier Science, Vol. 58, 2015, pp. 13–23.

P. Shanmugavadivu, P. Sumathy, and A. Vadivel, “Image Retrieval from Distributed Environment Using Geometric Features”, IEEE – International Conference on Advances in Engineering, Science and Management, Nagappattinam, India, IEEE –ICAESM, 2012, pp. 750 – 754.

A. Ben Ayed, S. Kardouchi and S. A. Selouani, “Rotation invariant Fuzzy Shape Contexts based on Eigen shapes and Fourier transforms for efficient Radiological image retrieval”, in Proceedings of International Conference on Multimedia Computing and Systems (ICMCS), Tangier, 2012, pp. 266 –271.

K. S Zou, C. K. Chan, S. X. Peng, A. Luximon, Z. Q. Chen and W. H. Ip, “Shape-based retrieval and analysis of 3D models using fuzzy weighted symmetrical depth images”, Neuro computing, Elsevier Science, Vol. 89, 2012, pp. 114–121.

G. Castellano, A. M. Fanelli and M. A. Torsello, “Shape Annotation by Semi-supervised Fuzzy Clustering”, Information Sciences, Elsevier Science, Vol. 289, 2014, pp. 148–161.

T. T. Tran, V. T. Pham and K. K. Shyu, “Image segmentation using fuzzy energy-based active contour with shape prior”, Journal of Visual Communication Image Representation, Elsevier Science, Vol. 25, No. 7, 2014, pp. 1732–1745.

A. Tanács, J. Lindblad, N. Sladoje and Z. Kato,“ Estimation of linear deformations of 2D and 3D fuzzy objects”, Pattern Recognition, Elsevier Science, Vol. 48, No. 4, 2015, pp. 1391–1403.

Zhidong Deng, K. Xiao and Jing Huang, “A New Fuzzy Shape Context Approach Based on Multi-clue and State Reservoir Computing”, in Proceedings of International Joint Conference on Neural Networks (IJCNN’14), Beijing, China, 2014, pp. 2361-2366.

M. Hasegawa and S. Tabbone, “Amplitude-only log Radon transform for geometric invariant shape descriptor”, Pattern Recognition, Elsevier Science, Vol. 47, No. 2, 2014, pp. 643–658.

P. ShanmugaVadivu, P. Sumathy andA. Vadivel, FOSIR: Fuzzy-Object-Shape for Image Retrieval Applications, Nuero Computing, Elsevier Science, Vol. 171, No. 1, 2016, pp.719- 735.

S. Ardizzoni, I. Bartolini, M. Patella, “Windsurf: Region-Based Image Retrieval Using Wavelets, DEXA Workshop”, 1999, pp.167–173.

F. Jurie and B. Triggs, “Creating efficient codebooks for visual recognition”, in Proceedings of International Conference on Computer Vision, 2005, pp. 604-610.

D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree”, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2006, pp. 2161–2168.

B. Leibe, K. Mikolajczyk and B. Schiele, “Efficient clustering and matching for object class recognition”, in Proceedings of the British Machine Vision Conference”, 2006, pp. 789-798.

F. Moosmann, B. Triggs and F. Jurie, “Randomized clustering forests for image classification”, IEEE Transactions on Pattern analysis and Machine Intelligence, Vol. 30, No. 9, 2008, pp.1632-1646.

S. Kaski, J. Kangas and T. Kohonen, “Bibliography of Self-Organizing Map (SOM)”, Neural Computing Surveys, 1998, pp. 1981-1997.

X. Qian, and H. D. Tagare, “Adapting indexing trees to data distribution in feature spaces”, Journal of Computer Vision and Image Understanding, Vol. 114, No. 1, 2010, pp. 111-124.

M. Mejdoub, L. Fonteles, C. BenAmar and M. Antonini, “Embedded lattices tree: An efficient indexing scheme for content based retrieval on image databases”, Journal of Visual Communication and Image Representation, Vol. 20, No. 2, 2009, pp. 145–156.

M. Rusinol, A. Borras and J. Llados, “Relational indexing of Vectorial primitives for symbol spotting in line-drawing images”, Pattern Recognition Letters, Elsevier Science, Vol. 31, No. 3, 2010, pp. 188–201.

M. Liu and P-T. Yap, “Invariant representation of orientation fields for fingerprint indexing”, Pattern Recognition, Elsevier Science, Vol. 45, No. 7, 2012, pp. 2532–2542.

P. Poursistani, H. Nezamabadi-pour, R. AskariMoghadam and M. Saeed, “Image indexing and retrieval in JPEG compressed domain based on vector quantization”, Mathematical and Computer Modelling, Elsevier Science, Vol. 57, No. (5/6), 2013, pp.1005–1017.

J. M. Barrios, B. Bustos and T. Skopal, “Analyzing and dynamically indexing the query set”, Information Systems, Elsevier Science. Vol. 45, 2014, pp. 37–47.


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