Abstract
Immense increase in digital images demands an effective image retrieval system. In text based image retrieval systems, textual keywords are used for indexing. The query keywords are matched with the keywords associated with images to perform image retrieval task. Users usually demand higher proportion of the query keywords in the retrieved images than the other undesired keywords. Existing image retrieval systems retrieve images that do not contain the query keywords either in equal or higher proportion than other keywords. This paper proposes a new image retrieval system that applies fuzzy ontology and uncertain frequent pattern mining for image retrieval to resolve this issue. Image content is represented in terms of concepts and categories. Fuzzy ontology is constructed by utilizing the concepts and categories associated with the images. Uncertain frequent pattern mining is then applied on the association that exists among the concepts in images. These patterns assist in retrieving images, which contain the required query keywords in high proportion than other keywords. The ranking of retrieved images is evaluated with two different measures, i.e., mean and variance and difference at various thresholds of concepts’ occurrences in images. Experimental results show that the proposed image retrieval system performs better than existing ontology based retrieval systems.




















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Huu QN, Thuy QDT, Phuong Van C, Van CN, Quoc TN (2018) An efficient image retrieval method using adaptive weights. Appl Intell:1–20
Jasmine KP, Kumar PR (2014) Localized Rgb color histogram feature descriptor for image retrieval. International Journal of Advances in Engineering & Technology 7:887
Huang J, Zabih R (1998) Color-spatial image indexing and applications. Cornell University, Ithaca
Kaur H, Jyoti K (2013) Survey of techniques of high level semantic based image retrieval. IJRCCT 2(1):15–19
Liu Y, Zhang D, Lu G, Ma W-Y (2007) A survey of content-based image retrieval with high-level semantics. Pattern Recogn 40(1):262–282
Wang X-Y, Yang H-Y, Li D-M (2013) A new content-based image retrieval technique using color and texture information. Comput Electr Eng 39(3):746–761
Smeaton AF (2012) Information retrieval and hypertext. Springer Science \& Business Media
Liaqat M. (2013) Image classification and retrieval based on crisp and Fuzzy ontology. In: Computer, Control \& Communication (IC4), 2013 3rd International Conference on, p 1–6
Bchir O, Ismail MMB, Aljam H (2018) Region-based image retrieval using relevance feature weights. International Journal of Fuzzy Logic and Intelligent Systems 18:65–77
Chen L, Xu D, Tsang IW, Luo J (2010) Tag-based web photo retrieval improved by batch mode re-tagging. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, p 3440–3446
Liu Y, Xu D, Tsang IW, Luo J (2011) Textual query of personal photos facilitated by large-scale web data. IEEE Trans Pattern Anal Mach Intell 33(5):1022–1036
Park K-W, Jeong J-W, Lee D-H (2007) OLYBIA: ontology-based automatic image annotation system using semantic inference rules. Advances in databases: concepts, systems and applications, p 485–496
Town C (2006) Ontological inference for image and video analysis. Mach Vis Appl 17(2):94–115
Liaqat M, Khan S, Majid M (2016) Fuzzy ontology based model for image retrieval. In: International Conference on Mobile Web and Information Systems, p 108–120
Chen H, Trouve A, Murakami KJ, Fukuda A (2017) An intelligent annotation-based image retrieval system based on RDF descriptions. Comput Electr Eng 58:537–550
Castellano G, Fanelli AM, Sforza G, Torsello MA (2016) Shape annotation for intelligent image retrieval. Appl Intell 44:179–195
Hakimpour F, Timpf S (2001) Using ontologies for resolution of semantic heterogeneity in GIS. In: Proceedings of 4th AGILE Conference on Geographic Information Science, p 385–395
Liaqat M, Khan S, Majid M (2017) Image retrieval based on fuzzy ontology. Multimed Tools Appl:1–23
Mei T, Rui Y, Li S, Tian Q (2014) Multimedia search reranking: a literature survey. ACM Comput Surv 46(3):38
Wei S, Zhao Y, Zhu Z, Liu N (2010) Multimodal fusion for video search reranking. IEEE Trans Knowl Data Eng 22(8):1191–1199
Hua G, Tian Q (2009) What can visual content analysis do for text based image search? In: Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on, p 1480–1483
Liu Y, Mei T, Hua X-S (2009) CrowdReranking: exploring multiple search engines for visual search reranking. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, p 500–507
Zhou XS, Huang TS (2003) Relevance feedback in image retrieval: a comprehensive review. Multimedia Systems 8(6):536–544
Lee G, Yun U (2017) A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives. Futur Gener Comput Syst 68:89–110
Li S, Purushotham S, Chen C, Ren Y, Kuo C-CJ (2017) Measuring and predicting tag importance for image retrieval. IEEE Trans Pattern Anal Mach Intell
Vogel J, Schiele B (2007) Semantic modeling of natural scenes for content-based image retrieval. Int J Comput Vis 72(2):133–157
Elazary L, Itti L (2008) Interesting objects are visually salient. J Vis 8(3):3–3
Berg AC, Berg TL, Daume H et al (2012) Understanding and predicting importance in images. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, p 3562–3569
Luo B, Wang X, Tang X (2003) World Wide Web based image search engine using text and image content features. In: Electronic Imaging 2003, p 123–130
Bukhari AC, Kim Y-G (2013) A research on an intelligent multipurpose fuzzy semantic enhanced 3D virtual reality simulator for complex maritime missions. Appl Intell 38:193–209
Ali F, Kim EK, Kim Y-G (2015) Type-2 fuzzy ontology-based opinion mining and information extraction: a proposal to automate the hotel reservation system. Appl Intell 42:481–500
Styrman A (2005) Ontology-based image annotation and retrieval
Allani O, Zghal HB, Mellouli N, Akdag H (2016) A knowledge-based image retrieval system integrating semantic and visual features. Procedia Comput Sci 96:1428–1436
Pereira R, Ricarte I, Gomide F (2006) Fuzzy relational ontological model in information search systems. Capturing Intelligence 1:395–412
Pereira R, Ricarte I, Gomide F (2009) Information retrieval with FROM: the fuzzy relational ontological model. Int J Intell Syst 24:340–356
Marcoulides GA (2005) Discovering knowledge in data: an introduction to data mining. Taylor & Francis, Abingdon
Bonchi F, Lucchese C (2005) Pushing tougher constraints in frequent pattern mining. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, p 114–124
Giannella C, Han J, Pei J, Yan X, Yu PS (2003) Mining frequent patterns in data streams at multiple time granularities. Next generation data mining. 212:191–212
Leung C, Irani P, Carmichael C (2008) FIsViz: a frequent itemset visualizer. Advances in knowledge discovery and data mining, p 644–652
Pei J, Han J, Mao R, et al (2000) CLOSET: an efficient algorithm for mining frequent closed itemsets. In: ACM SIGMOD workshop on research issues in data mining and knowledge discovery, p 21–30
Leung, Carson, Mateo, Mark, and Brajczuk, Dale (2008) A tree-based approach for frequent pattern mining from uncertain data. Advances in knowledge discovery and data mining, p 653–661
Lin C-W, Hong T-P (2012) A new mining approach for uncertain databases using CUFP trees. Expert Syst Appl 39(4):4084–4093
Wang L, Feng L, Wu M (2013) AT-mine: an efficient algorithm of frequent itemset mining on uncertain dataset. J Comput 8(6):1417–1427
Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Hong T-P, Fujita H (2018) A survey of incremental high-utility itemset mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8:e1242
Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Fujita H (2018) Extracting non-redundant correlated purchase behaviors by utility measure. Knowl-Based Syst 143:30–41
Yun U, Kim D, Yoon E, Fujita H (2018) Damped window based high average utility pattern mining over data streams. Knowl-Based Syst 144:188–205
Yun U, Ryang H, Lee G, Fujita H (2017) An efficient algorithm for mining high utility patterns from incremental databases with one database scan. Knowl-Based Syst:188–206
Yang J, Zhang Y, Wei Y (2015) an improved vertical algorithm for frequent itemset mining from uncertain database. Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2017 9th International Conference on, IEEE
Zhang B, Lin JC-W, Fournier-Viger P, Li T (2017) Mining of high utility-probability sequential patterns from uncertain databases. PLoS One 12:e0180931
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Liaqat, M., Khan, S., Younis, M.S. et al. Applying uncertain frequent pattern mining to improve ranking of retrieved images. Appl Intell 49, 2982–3001 (2019). https://doi.org/10.1007/s10489-019-01412-9
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DOI: https://doi.org/10.1007/s10489-019-01412-9