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A survey of content-based image retrieval with high-level semantics
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“The performance of a CBIR system mainly depends on these selected features [7].”
From Medical image retrieval using deep convolutional neural network (2017) · cites this paper as [7]
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Lau, S. L., Lew, J., Ho, C. C., & Su, S. M. (2021) · IEEE International Conference on Computing (ICOCO) · doi:10.1109/icoco53166.2021.9673553 · Cited by 4
A Comparative Analysis of Retrieval Techniques in Content Based Image Retrieval
Sardey, M. P., & Kharate, G. K. (2015) · doi:10.5121/csit.2015.51410 · Cited by 4
Semantic lattices for multiple annotation of images
Tousch, A.-M., Herbin, S., & Audibert, J.-Y. (2008) · Multimedia Information Retrieval · doi:10.1145/1460096.1460152 · Cited by 4
Inchoative integration of content based image retrieval: Shodhani
Khan, A., Deep, S., Li, J., Memon, M. H., Shaikh, R. A., & Kumar, K. (2014) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip.2014.7073410 · Cited by 4
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Fazal-e-Malik, Baharudin, B., & Ullah, K. (2011) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit.2011.24 · Cited by 4
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Satta, R., & Ciardulli, A. (2015) · IET Computer Vision · doi:10.1049/iet-cvi.2014.0320 · Cited by 4
Content Based Image Retrieval with Relevance Feedback Using Riemannian Manifolds
Patil, P. B., & Kokare, M. (2014) · doi:10.1109/icsip.2014.9 · Cited by 4
“Comprehensive surveys in RF in CBIR can be found in [7]-[8].”
BackgroundAn efficient content based image retrieval using edge orientation co-occurrence matrix
Gholipour, F., & Ebrahimnezhad, H. (2014) · International Conference on Information and Knowledge Technology (IKT) · doi:10.1109/ikt.2014.7030335 · Cited by 4
Image retrieval by comparison between complete oriented graphs of fuzzy regions
Gallas, A., Barhoumi, W., & Zagrouba, E. (2012) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp.2012.6356182 · Cited by 4
“In other cases, the image regions were organized in a specific structure [3].”
BackgroundVideo scene classification based on natural language description
Zhang, L., Khan, M. U. G., & Gotoh, Y. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130353 · Cited by 4
“In video processing, identification of high level semantic concepts has been a hot issue in recent years, further leading to studies such as video classification, summarisation and retrieval [7, 13, 14].”
BackgroundDesign of Shopping Guide System with Image Retrieval Based on Mobile Platform
Zhou, D., Hu, B., Wang, Q., Hu, B., Jia, L., Wu, Y., & Xie, L. (2013) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/3ca-13.2013.37 · Cited by 4
Study on forensic image retrieval
Yuan, H., & Liu, Y. (2014) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2014.6931137 · Cited by 4
“And a few of commercial and experimental prototype systems have been developed, such as QBIC, Photobook, Virage, VisualSEEK, Netra, SIMPLIcity [5].”
BackgroundFeature decreasing methods using fuzzy rough set based on mutual information
Lotfabadi, M. S., Shiratuddin, M. F., & Wong, K. W. (2013) · IEEE Conference on Industrial Electronics and Applications (ICIEA) · doi:10.1109/iciea.2013.6566538 · Cited by 4
“This high dimensionality of the feature vectors creates problems in constructing efficient data structures for search and retrieval [3].”
BackgroundAn improved image segmentation algorithm for salient object detection
Liu, Y., Zhang, J., Tjondronegoro, D., Geva, S., & Li, Z. (2008) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz.2008.4762141 · Cited by 4
“According to a recent survey, JSEG seems to be the most widely used method in natural image segmentation so far [6].”
MethodA New Approach for Interactive Image Retrieval Based on Fuzzy Feedback and Support Vector Machine
Javidi, M., Aski, B. S., Homaei, H., & Pourreza, H. R. (2008) · Computational Intelligence for Modelling, Control and Automation · doi:10.1109/cimca.2008.176 · Cited by 4
Semantic Strategic Satellite Image Retrieval
Messaoudi, W., Farah, I. R., Ettabaâ, K. S., & Solaiman, B. (2008) · International Conference on Information and Communication Technologies: From Theory to Applications (ICTTA) · doi:10.1109/ictta.2008.4529997 · Cited by 4
Ensemble of Two-Class Classifiers for Image Annotation
Li, Y., Lu, J., Zhang, Y., Li, R., & Xu, W. (2008) · Education Technology and Training · doi:10.1109/ettandgrs.2008.320 · Cited by 4
“CBIR computes relevance based on the visual similarity of image low-level features such as color, texture and shape [1,2].”
BackgroundMean BoF per Quadrant - Simple and Effective Way to Embed Spatial Information in Bag of Features
Sosa-García, J., & Odone, F. (2015) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0005281002970304 · Cited by 4
“(CBIR) has been a very active research area (Liu et al., 2007; Rui et al., 1999).”
BackgroundLee, S., & Kang, S. (2016) · Journal of the Korean Physical Society · doi:10.3938/jkps.69.1254 · Cited by 4
Compact Composite Descriptors for Content Based Image Retrieval
Mustapha, S., & Jalab, H. A. (2012) · International Conference on Advanced Computer Science Applications and Technologies · doi:10.1109/acsat.2012.34 · Cited by 4
“In the mathematical sense, precision is the fraction of the relevant images for an image query amongst all images retrieved [42].”
BackgroundA Model for Qualitative Colour Description and Comparison
Falomir, Z., Ortega, J. A., & Morente, F. V. (2011) · Deposito de Investigacion Universidad de Sevilla (University of Seville) · Cited by 4
Privacy preserving in CBIR using color and texture features
Majhi, M., & Maheshkar, S. (2016) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc.2016.7913159 · Cited by 4
“[15] described that shape represents the contour of the questionable object in an image which is invariant to location and size of the objects.”
BackgroundMa, B., Xu, T., & Zhou, J. (2017) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2017.8124526 · Cited by 4
National costume image retrieval based on integrated region matching
Zhao, W., Zhou, J., & Xu, T. (2017) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/siprocess.2017.8124527 · Cited by 4
Research Issues for Next Generation Content-Based Image Retrieval
Tyagi, V. (2017) · Springer eBooks · doi:10.1007/978-981-10-6759-4_15 · Cited by 4
UNESCO Intangible Cultural Heritage Management on the Web
Artese, M. T., & Gagliardi, I. (2014) · Advances in information quality and management · doi:10.4018/978-1-4666-5888-2.ch527 · Cited by 4
Adegbola, O. A., Aborisade, D. O., Popoola, S. I., & Atayero, A. A. A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95171-3_42 · Cited by 4
Software Framework for Fast Image Retrieval
Grycuk, R., & Scherer, R. (2019) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar.2019.8864722 · Cited by 4
“In order to retrieve images more accurately [12] separated image objects [28][29] need to be taken under consideration.”
BackgroundA flexible content‐based image retrieval model and a customizable system for the retrieval of shapes
Bordogna, G., & Pagani, M. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21286 · Cited by 4
“In fact, a main problem of current CBIR systems is their inability to cope with the semantic gap existing between the system’s interpretation of the main visual feature in an image, and the human’s image perception (Liu et al., 2007; Rongrong,Yao, & Liang, 2008; Smeulders et al., 2000).”
BackgroundMethodInfluentialApplication-Driven Near-Data Processing for Similarity SearchPreprint
Lee, V. T., Mazumdar, A., Mundo, C. C. D., Alaghi, A., Ceze, L., & Oskin, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.03742 · Cited by 4
“We use content-based search [3] as an example application where kNN performance is vitally important.”
BackgroundMethodMulti modal multi-semantic image retrieval
Kesorn, K. (2010) · Queen Mary Research Online (Queen Mary University of London) · Cited by 4
Li, Y., Liu, W., Li, E., Zhang, L., & Li, X. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3601739 · Cited by 3
“The fused prompts and image features are passed to the precise decoder to address the challenge of accurately outlining the boundaries of target objects in complex backgrounds [22].”
Accelerating Image Classification based on a Model for Estimating Descriptor-to-Class Distance
Gorokhovatskyi, V., Gadetska, S., & Stiahlyk, N. (2023) · International Journal of Computing · doi:10.47839/ijc.22.4.3355 · Cited by 3
Semantic labeling of social big media using distributed online robust classification
Sadigh, A. N., Bahraini, T., & Yazdi, H. S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107928 · Cited by 3
Recent advances in content based image retrieval using deep learning techniques: A survey
Ahmed, A. S. F., & Ibraheem, I. N. (2024) · AIP conference proceedings · doi:10.1063/5.0236594 · Cited by 4
Sequential citation counts prediction enhanced by dynamic contents
He, G., Gu, S., Xue, Z., Duan, Y., & Zhu, X. (2025) · Journal of Informetrics · doi:10.1016/j.joi.2025.101645 · Cited by 3
Scene Classification Using Efficient Low-level Feature Selection 1
Lee, C.-H., & Hsu, C.-H. (2008) · Cited by 3
Semantic Categorization and Retrieval of Natural Scene Images
Ronge, S. D., & Laukar, C. (2013) · Cited by 3
Image segmentation approach in multimodal information retrieval
Ahmed, S. R., Li, J., Hammad, M. M., & Asif, K. (2013) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip.2013.6716624 · Cited by 3
“[10] are learning from user interaction in a Relevance Feedback loop [9] and extracting semantic features in basketball match for a low-level features to match with high level features[13].”
BackgroundDeljooi, H., & Moghadam, A. M. E. (2012) · CSI International Symposium on Artificial Intelligence and Signal Processing · doi:10.1109/aisp.2012.6313726 · Cited by 3
“Thus there is a difference between high level concept usedby humans to interpret images and low level features that called semantic gap [4].”
BackgroundColor Image Retrieval Using Color Histogram and Orthogonal Combination of Linear Binary Pattern
Kaur, K. P., Singh, C., & Bhullar, E. W. (2014) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/2683483.2683531 · Cited by 3
“In the text based image retrieval system, database images are represented by adding text strings [1].”
MethodAccurate Approximation of the Earth Mover’s Distance in Linear Time
Jang, M.-H., Kim, S.-W., Faloutsos, C., & Park, S. (2014) · Journal of Computer Science and Technology · doi:10.1007/s11390-014-1417-x · Cited by 3
“Keywords earth mover’s distance, approximation, content-based image retrieval”
BackgroundHigh Level Semantic Retrieval of Thangka Image Based on C-K Relation Net
Wang, W., Qian, J., & Yin, L. (2010) · International Multi-Conference on Computing in the Global Information Technology (ICCGI) · doi:10.1109/iccgi.2010.40 · Cited by 3
“The state-of-the-art techniques for reducing the “semantic gap” can be roughly classified into five categories [1]: (1) Using machine learning tools to associate low-level image features with high-level semantics [2][3][4]; (2) Introducing relevance feedback into retrieval loop for continuous learning of users’ intent…”
MethodGenetic Optimization for Associative Semantic Ranking Models of Satellite Images by Land Cover
Barb, A. S., & Kilicay-Ergin, N. H. (2013) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi2020531 · Cited by 3
“A comprehensive review of various semantic models are provided in [6] where methods for reducing the semantic gap include using object ontology to define concepts, using machine learning methods to associate low-level features to users’ semantics, introducing relevance feedback to learn users’ intentions, generating a…”
MethodObject-driven image group annotation
Baba, T., & Chen, T. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5652594 · Cited by 3
“To retrieve desired photos from a large number of stored photos, as shown in Fig. 1, automatic annotation about object or scene categories for these photos is a very important technology [ 1 ][2].”
A fast multi-model approach for object duplicate extraction
Piccinini, P., Prati, A., & Cucchiara, R. (2009) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2009.5403114 · Cited by 3
“Object extraction and localization are crucial tasks for several computer vision applications, such as object recognition [2], image/video retrieval [9], automatic robot navigation [13], or pick-and-place for industrial applications [11].”
BackgroundImage Retrieval with Semantic Sketches
Engel, D., Herdtweck, C., Browatzki, B., & Curio, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23774-4_35 · Cited by 3
“Further reviews on semantic image retrieval can be found in [10, 11].”
BackgroundImage Annotation Based on Feature Weight Selection
Zhao, T., Lu, J., Zhang, Y., & Xiao, Q. (2008) · International Conference on Cyberworlds (CW) · doi:10.1109/cw.2008.49 · Cited by 3
“Content-based image retrieval computes relevance based on the visual similarity of low-level image features such as colors, textures and shapes [1].”
BackgroundMethodImage Characterization via Multilayer Neural Networks
Barcelos, C. A. Z., Ribeiro, E. F., & Batista, M. A. (2008) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2008.71 · Cited by 3
“Neural Networks have been used in many image retrieval and characterization applications mainly to generate feature vectors also called “high-level semantic vectors” through image classification by machine learning [ 6 ].”
MethodMurakami, T., Kageyama, Y., Nishida, M., & Shirasawa, Y. (2012) · Society of Instrument and Control Engineers of Japan · Cited by 3
Semantic Retrieval of Radiological Images with Relevance Feedback
Kurtz, C., Idoux, P.-A., Thangali, A., Cloppet, F., Beaulieu, C. F., & Rubin, D. L. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24471-6_2 · Cited by 3
“As the system captures the user’s intention when a new query is performed, the resulting set of images can be continually improved until the gain flattens, according to the iterative learning process [14].”
MethodREGION AND LEARNING BASED RETRIEVAL FOR MULTI-MODALITY MEDICAL IMAGES
Song, Y., Cai, W., Eberl, S., FULHAM, M. J., & Feng, D. D. (2010) · Biomedical Engineering · doi:10.2316/p.2010.723-063 · Cited by 3
“As regions of a certain level of homogeneity of texture and shape are often considered to represent an object in an image, region-based approaches have gained favor in problem domains where images contain some distinctive objects amidst background [4, 5].”
BackgroundA Hierarchical Multilevel Image Thresholding Method Based on the Maximum Fuzzy Entropy Principle
Guan, P. P., & Yan, H. (2012) · IGI Global eBooks · doi:10.4018/978-1-4666-2518-1.ch010 · Cited by 3
Seetharaman, K. M. R., & Sathiamoorthy, S. (2014) · Cited by 3
Combining hybrid information descriptors and DCT for improved CBIR performance
Singh, V. P., Malhotra, S., & Srivastava, R. (2016) · International Conference on Control, Computing, Communication and Materials (ICCCCM) · doi:10.1109/iccccm.2016.7918219 · Cited by 3
Face Retrieval Framework Relying on User's Visual Memory
Sato, Y., Fukusato, T., & Morishima, S. (2018) · International Conference on Multimedia Retrieval · doi:10.1145/3206025.3206038 · Cited by 3
Private Content-based Image Query System using Statistical Properties
Ramola, A., & Shakya, A. K. (2017) · International Conference on Innovations in Control, Communication and Information Systems (ICICCI) · doi:10.1109/iciccis.2017.8660951 · Cited by 3
“content-based image retrieval (CBIR) systems have been developed[3][4][5][6][7].”
The Research of Image Retrieval based on Multi Feature DS Evidence Theory Fusion
Chang, C. H., Yu, X., & Guang, Y. (2016) · International Journal of Signal Processing Image Processing and Pattern Recognition · doi:10.14257/ijsip.2016.9.1.06 · Cited by 3
“Exporting the similar image according to the specified requirements of users [10].”
BackgroundWavelet TRANSFORMATION for Content-Based Image Retrieval COMBINE G-Regions Of Interest
Xugang, Yin, F., & Wei, C. (2011) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-25986-9_45 · Cited by 3
An analysis of content-based image retrieval
Koyuncu, H., Dixit, M., & Koyuncu, B. (2021) · International Advanced Researches and Engineering Journal · doi:10.35860/iarej.811927 · Cited by 3
Kim, K., Park, C., Seo, J., & Lim, H. (2021) · IEEE Access · doi:10.1109/access.2021.3122547 · Cited by 3
“…because the low-level features extracted directly from a given input The associate editor coordinating the review of this manuscript and approving it for publication was G. R. Sinha . image cannot be interpreted or used to determine what the user wants to search for, given just the input image [1].”
BackgroundDang, Q., Zhao, B., Sun, B., Yu, Q., & Du, C. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-7769-5_12 · Cited by 3
Sucharitha, G., Kalyani, B. J. D., Sekhar, G. C., & Srividya, C. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-8493-8_21 · Cited by 3
Image Retrieval System Using Kirsch based Local Ternary Pattern
Agarwal, M. (2023) · Advances in Electrical and Electronic Engineering · doi:10.15598/aeee.v21i1.4706 · Cited by 3
“Content Based Image Retrieval (CBIR) solves this problem and enables automatic matching of images and shows required set of images in a very less time [1] and [2].”
Kong, M., & Wang, F. (2024) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2024.100960 · Cited by 3
Interactive content-based Document Retrieval using fuzzy attributed relational graph matching
Chaieb, R., Kalti, K., & Amara, N. E. B. (2015) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2015.7333896 · Cited by 3
Multilevel haar wavelet transform and histogram based relevant image retrieval system
Dhotre, D. R., Bamnote, G. R., & Shekokar, P. H. (2017) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2017.8282622 · Cited by 3
“A lot of exploration work has been carried out on Image Retrieval by many researchers, expanding in both depth and breadth [26]-[30].”
BackgroundSemantics Based Web Ranking Using a Robust Weight Scheme
Priya, R. V., Vijayakumar, V., & Yang, L. (2018) · International Journal of Web Portals · doi:10.4018/ijwp.2019010104 · Cited by 3
Accentuating Features of Description Logics in High-Level Interpretations of Hand-Drawn Sketches
Abdelghaffar, N. M., Abdelfattah, A. M. H., Taha, A. A., & Khamis, S. M. (2019) · Künstliche Intell. · doi:10.1007/s13218-019-00602-4 · Cited by 3
“This lies at the heart of the so-called semantic gap [2], which is fundamental for AI to overcome.”
BackgroundSavakar, D. G., Hosur, R., & Pawar, D. M. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-9187-3_24 · Cited by 3
Evaluating Boundary Conditions and Hierarchical Visualization in CBIR
Real, L. G. D. S., Bueno, R., & Ribeiro, M. X. (2019) · International Conference on Information Visualisation (IV) · doi:10.1109/iv.2019.00021 · Cited by 3
“In this sense, by the use of metrics to measure the similarity between two feature vectors, CBIR systems retrieve images that are closer to the given query image.”
BackgroundInfluentialA Visual Saliency-Based Approach for Content-Based Image Retrieval
Khan, A., & Jalal, A. S. (2020) · International Journal of Cognitive Informatics and Natural Intelligence · doi:10.4018/ijcini.2021010101 · Cited by 3
Visualization of similarity queries with trajectory estimation in complex data
Paiva, C. E., Malaquias, R. D., & Bueno, R. (2020) · International Conference on Information Visualisation (IV) · doi:10.1109/iv51561.2020.00025 · Cited by 3
Sajwan, V., & Ranjan, R. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-9113-3_2 · Cited by 3
A New Approach to Interactive Visual Search with RBF Networks Based on Preference Modelling
Rotter, P., & Skulimowski, A. M. J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-69731-2_82 · Cited by 3
Content Based Retrieval Systems in a Clinical Context
Valente, F., Costa, C. M. A., & Silv, A. (2013) · InTech eBooks · doi:10.5772/53027 · Cited by 3
Modified angular radial partitioning for edge image description in image retrieval
An, S. Y., Lee, L. K., & Oh, S. Y. (2012) · Electronics Letters · doi:10.1049/el.2012.0705 · Cited by 3
Building a Compact Relevant Sample Coverage for Relevance Feedback in Content-Based Image Retrieval
Yao, B., Ai, H., & Lao, S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_53 · Cited by 3
“Compared with object ontology and machine learning, which mainly rely on offline learning, RF is an online approach and has been shown to be effective in boosting image retrieval accuracy [2].”
BackgroundMethodContent Analysis from User's Relevance Feedback for Content-Based Image Retrieval
Wei, C.-H., & Li, C. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-174-2.ch010 · Cited by 3
“Another challenging issue arises from the difference between two descriptions of an object by high-level semantics and representations of low-level pixel data (Liu, Zhang, Lu, & Ma, 2007; Vasconcelos, 2007).”
BackgroundRahman, M. (2011) · InTech eBooks · doi:10.5772/13980 · Cited by 3
“CBIR computes relevance of query and database images based on the visual similarity of low-level features (e.g., color, texture, shape, edge, etc.) derived entirely from the images Smeulders et al. (2000); Liua et al. (2007); Datta et al. (2008).”
BackgroundMethodMultimodal Markov Random Field for Image Reranking Based on Relevance Feedback
Chavéz-García, R. O., Escalante, H. J., Montes-y-Gómez, M., & Sucar, L. E. (2013) · ISRN Machine Vision · doi:10.1155/2013/428746 · Cited by 3
“However, because of the difficulty of the task and of the subjectivity of user intentionmodeling, retrieval models are often equipped with automatic or manual post-retrieval mechanisms that aim at refining and improving the outputs of retrieval systems [4, 10, 11].”
BackgroundMethodA semantic approach to text-based image retrieval using a lexical ontology
Fadzli, S. A., & Setchi, R. (2010) · Cited by 3
A Clustering Approach to Image Retrieval Using Range Based Query and Mahalanobis Distance
Banerjee, M., Bandyopadhyay, S., & Pal, S. K. (2012) · Intelligent systems reference library · doi:10.1007/978-3-642-30341-8_6 · Cited by 3
A Fuzzy Mapping from Image Texture to Affective Thesaurus
Li, H., Li, J., Song, J., & Chen, J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74769-7_39 · Cited by 3
“Hence, texture is an important feature in defining high-level semantics for image retrieval purpose [ 4 ].”
BackgroundConference knowledge modeling for conference-video-recordings querying & visualization
Sokhn, M., Carrino, F., Mugellini, E., Khaled, O. A., & Serhrouchni, A. (2009) · International Conference on Management of Emergent Digital EcoSystems (MEDES) · doi:10.1145/1643823.1643865 · Cited by 3
“Other papers presented similar definitions [3, 4, 5].”
A hierarchical manifold subgraph ranking system for content-based image retrieval
Chang, R., & Qi, X. (2013) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2013.6607466 · Cited by 3
“Index Terms— Content based image retrieval (CBIR), hierarchical manifold subgraphs, semantic features, users’ relevance feedback group”
BackgroundAn Intelligent Web Digital Image Metadata Service Platform for Social Curation Commerce Environment
Hong, S.-Y., & Lee, S. (2015) · Modelling and Simulation in Engineering · doi:10.1155/2015/651428 · Cited by 3
Snyder, J. (2012) · iConference · doi:10.1145/2132176.2132178 · Cited by 3
An effective approach to identify digitized IWPs (ink and wash paintings)
Sheng, J. (2012) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2012.6469719 · Cited by 3
“Consequently, although content-based image retrieval (CBIR) has been widely developed in the recent years [2], those methods cannot be directly applied to the study of IWPs.”
MethodRetrieving and ranking unannotated images through collaboratively mining online search results
Xu, S., Jiang, H., & Lau, F. C.-M. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2063650 · Cited by 3
Cheruiyot, W. K., Tan, G., Musau, F., & Mushi, J. C. (2011) · Multimedia Systems · doi:10.1007/s00530-011-0231-3 · Cited by 3
“The Si’s are called the singular values of X, which indicates the weight, or importance, of a dimension [3].”
BackgroundMethodInfluentialRahman, M. M., & Bhattacharya, P. (2009) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1646396.1646422 · Cited by 3
“To narrow the semantic gap, we have witnessed new trends over the last few years in the form of semantic image classiflcation, annotation, and interactive RF-based retrieval by utilizing various ofi-line and on-line learningbased techniques [ 10 ,11,21].”
MethodTurning Experience Products into Search Products: Making User Feedback Count
Selke, J., & Balke, W. (2011) · IEEE Conference on Commerce and Enterprise Computing (CEC) · doi:10.1109/cec.2011.17 · Cited by 3
“Feature extractors aim to identify those properties of objects that closely resemble human visual and auditory perception [23], [24].”
BackgroundSANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction
Vijendran, A. S., & Deepa, C. (2014) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2015.01.05 · Cited by 3
“It needs the integration of relevant lowlevel attribute mining, effective knowledge of high-level semantics, friendly user interface, and efficient indexing tool [6].”
BackgroundIncremental on-line semantic indexing for image retrieval in dynamic databases
Karthik, S., Pulla, C., & Jawahar, C. V. (2009) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2009.5204246 · Cited by 3
Gao, P., Lu, Y., Qiu, C., Li, N., Wenhua, Y., & Rui, L. (2008) · International Symposium on Computer Science and Computational Technology · doi:10.1109/iscsct.2008.42 · Cited by 3
“However, we can not access or make good use of the information unless it is organized so as to allow efficient browsing, searching, and retrieval.”
BackgroundTowards ontology-based retrieval of historical images
Coustaty, M., Tsopzé, N., Bouju, A., Bertet, K., & Louis, G. (2015) · Applied Ontology · doi:10.3233/ao-150146 · Cited by 3
“However, we used a UML model to define the spatial part of the ontology which was transformed into an ontology in Malki et al. (2009) using the eclipseuml2owl tool.”
BackgroundMethodInfluentialContent -based Image Retrieval for Image Indexing
Al-Amin, M. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.060611 · Cited by 3
“INTRODUCTION Content-based image retrieval is emerging tremendous interest in image processing, computer graphics, computer vision, pattern recognition, image management system, and so on [1-4].”
BackgroundDescriptor Trends in Texture Classification for Material Recognition
Ayad, H., Abdulameer, M. H., George, L. E., & Hassan, N. F. (2015) · Research Journal of Applied Sciences Engineering and Technology · doi:10.19026/rjaset.10.1878 · Cited by 3
“The advantage of the combination feature is that each feature complements the other, improving the system’s retrieval accuracy and making the system’s retrieval more intelligent (Liu et al., 2007; Wu and Wu, 2009; Chih-Fong and WeiChao, 2009; Pujari et al., 2010).”
BackgroundA Review on Content-Based Image Retrieval Representation and Description for Fish
Osman, N. S., & Mustaffa, M. R. (2015) · International Conference on Advanced Computer Science Applications and Technologies · doi:10.1109/acsat.2015.15 · Cited by 3
“The way how computer and human interpret the images are different, and the interpretation gap is referred as ‘semantic gap’ [1].”
BackgroundMethodInfluential
Metadata
- Authors
- Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y.
- Affiliations
- Monash University (AU)
- Microsoft Research Asia (China) (CN)
- Corresponding author
- Liu, Ying
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2007-01
- Volume
- 40
- Issue
- 1
- Pages
- 262-282
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2046589280
- Cited by
- 1,696
- References
- 156
- Citations by year
- 2026: 12 · 2025: 36 · 2024: 37 · 2023: 49 · 2022: 44 · 2021: 73 · 2020: 73 · 2019: 91 · 2018: 109 · 2017: 132 · 2016: 129 · 2015: 137 · 2014: 138 · 2013: 121 · 2012: 130
- FWCI
- 67.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- content-based image retrieval, high-level semantics, semantic gap, low-level feature extraction, similarity measurement, object ontology, relevance feedback, semantic templates, web image search, information retrieval evaluation
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