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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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Feature integration of EODH and Color-SIFT: Application to image retrieval based on codebook
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Zhou, J., Liu, X., Xu, T., Gan, J., & Liu, W. (2016) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-016-0597-9 · Cited by 57
“As a consequence, many image retrieval systems have been developed as reported in [1].”
BackgroundHypertuned Convolutional Neural Network Residual Model Based Content Based Image Retrival System
Singh, A. D., Dixit, A., & Singh, B. K. (2022) · International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP) · doi:10.1109/icfirtp56122.2022.10059435 · Cited by 57
Two-Stage Reranking for Remote Sensing Image Retrieval
Tang, X., Jiao, L., Emery, W. J., Liu, F., & Zhang, D. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2714676 · Cited by 56
“In practice, however, some of them are dissimilar to the query because of the existing well-known semantic gap [38] in the CBIR community.”
BackgroundExpert system design using wavelet and color vocabulary trees for image retrieval
Subrahmanyam, M., Maheshwari, R. P., & Raman, B. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.11.029 · Cited by 53
A Fast Image Retrieval Method Designed for Network Big Data
Yang, J., Jiang, B., Li, B., Tian, K., & Lv, Z. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2657545 · Cited by 53
“With the development of information retrieval technology [5]–[7] and commercial search technology [8] in recent years, database indexing and text retrieval techniques has become a general pattern.”
BackgroundRobust image matching via local graph structure consensus
Jiang, X., Xia, Y., Zhang, X.-P. S., & Ma, J. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108588 · Cited by 55
Retrieval of Remote Sensing Images with Pattern Spectra Descriptors
Bosilj, P., Aptoula, E., Lefèvre, S., & Ewa Kijak, C. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5120228 · Cited by 53
“For an in-depth study of the issues related to CBIR and a comprehensive review of the field, the reader is referred to [27].”
BackgroundA novel image retrieval method based on multi-trend structure descriptor
Zhao, M. Y., Zhang, H., & Sun, J. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.02.016 · Cited by 52
An efficient and robust approach for biomedical image retrieval using Zernike moments
Kumar, Y., Aggarwal, A., Tiwari, S., & Singh, K. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.08.018 · Cited by 52
Secure video retrieval using image query on an untrusted cloud
Yan, H., Chen, M., Hu, L. F., & Jia, C. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106782 · Cited by 51
Colors -- Messengers of Concepts
Jahanian, A., Keshvari, S., Vishwanathan, S. V. N., & Allebach, J. P. (2017) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3009924 · Cited by 59
“This gap has been a major point of discussion for over a decade [Smeulders et al. 2000; Sethi et al. 2001; Mojsilovic and Rogowitz 2001; Liu et al. 2007] and is considered to be “a major challenge to solve in the multimedia community” [Lindner and S¨usstrunk 2015].”
BackgroundHeterogeneity-Aware Local Binary Patterns for Retrieval of Histopathology Images
Erfankhah, H., Yazdi, M., Babaie, M., & Tizhoosh, H. R. (2019) · IEEE Access · doi:10.1109/access.2019.2897281 · Cited by 51
“To narrow the gap between computer similarity and human specialist similarity (semantic gap) in CBIR, domain-specific features have been applied broadly in every CBIR system, which is the most challanging problem [27].”
BackgroundKurtz, C., Depeursinge, A., Napel, S. A., Beaulieu, C. F., & Rubin, D. L. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.06.009 · Cited by 51
“Despite many efforts conducted to design innovative CBIR systems (Liu et al., 2007), two issues remain unsolved when using semantic descriptors to characterize medical images.”
Color Directional Local Quinary Patterns for Content Based Indexing and Retrieval
Vipparthi, S. K., & Nagar, S. K. (2014) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-014-0006-x · Cited by 50
“Widespread literature survey on CBIR is accessible in [1-4].”
BackgroundTekli, J. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2525768 · Cited by 50
“The main problems with this category of methods are: i) computational efficiency (low-level feature indexing and mapping is time consuming), and ii) the so-called semantic gap: lowlevel features are usually unable to capture the high-level semantic meaning in the image [105].”
BackgroundInfluentialTowards automatic generation of e-assessment using semantic web technologies
Čubrić, M., & Tošić, M. (2011) · University of Hertfordshire Research Archive (University of Hertfordshire) · Cited by 50
Local Gabor maximum edge position octal patterns for image retrieval
Vipparthi, S. K., Murala, S., Nagar, S. K., & Gonde, A. B. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.04.062 · Cited by 50
“[2] Ying L, Dengsheng Z, Guojun L, Wei-Ying M, A survey of content-based image retrieval with high-level semantics.”
BackgroundDeep hashing for multi-label image retrieval: a survey
Rodrigues, J., de Cristo, M. A. P., & Colonna, J. G. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09820-x · Cited by 50
Semantic analysis on social networks: A survey
Bayrakdar, S., Yücedağ, İ., Şimşek, M., & Doğru, İ. A. (2020) · International Journal of Communication Systems · doi:10.1002/dac.4424 · Cited by 51
“161,162 EXIF is one of the most common metadata standards for digital photos which are used by all digital cameras.”
BackgroundA fast and efficient image retrieval system based on color and texture features
Singh, C., & Kaur, K. P. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.10.002 · Cited by 48
Learning Image Similarity from Flickr Groups Using Fast Kernel Machines
Wang, G., Hoiem, D., & Forsyth, D. A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.29 · Cited by 48
“There is an extensive content-based image management literature, with recent reviews in [23], [10].”
BackgroundRotation Invariant Curvelet Features for Region Based Image Retrieval
Zhang, D., Islam, M. M., Lu, G., & Sumana, I. J. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0503-6 · Cited by 46
“…(Ngo et al. 2001; Lu et al. 2006), Wold texture (Long et al. 2003; Liu and Picard 1996), wavelet transform (Datta et al. 2008; Wang et al. 2001; Do and Vetterli 2002; Bhagavathy and Chhabra 2007; Suematsu et al. 2002) and Gabor filters (Manjunath and Ma 1996; Zhang and Lu 2000; Liu et al. 2007).”
InfluentialWasielewski, A. (2023) · The MIT Press eBooks · doi:10.7551/mitpress/14268.001.0001 · Cited by 56
Image Annotation by Input–Output Structural Grouping Sparsity
Han, Y., Wu, F. Q., Tian, Q., & Zhuang, Y. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2183880 · Cited by 47
“The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Joseph P. Havlicek.”
BackgroundFakhari, A., & Moghadam, A. M. E. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2012.10.019 · Cited by 47
Automatic image semantic interpretation using social action and tagging data
Sawant, N., Li, J., & Wang, J. Z. (2010) · Multimedia Tools and Applications · doi:10.1007/s11042-010-0650-8 · Cited by 47
“Detailed discussion about various local and global image features are available in [117, 166, 179].”
BackgroundFarsi, H., & Mohamadzadeh, S. (2013) · IET Image Processing · doi:10.1049/iet-ipr.2012.0203 · Cited by 47
“Dataset management (based on text feature) and machine vision (based on vision feature) are the two main research fi elds, which study the image retrieval system [1, 2].”
BackgroundPradhan, J., Kumar, S., Pal, A. K., & Banka, H. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2018.07.016 · Cited by 47
An AES-Based Secure Image Retrieval Scheme Using Random Mapping and BOW in Cloud Computing
Wang, H., Xia, Z., Fei, J., & Xiao, F. (2020) · IEEE Access · doi:10.1109/access.2020.2983194 · Cited by 47
“To achieve the efficient use of the image information, the researchers proposed some Content-based Image Retrieval (CBIR) schemes to improve the efficiency of retrieving similar images [1].”
MethodSingha, M., Hemachandran, K., & Paul, A. (2012) · IET Image Processing · doi:10.1049/iet-ipr.2011.0453 · Cited by 46
Improved support vector machine algorithm for heterogeneous data
Peng, S., Hu, Q., Chen, Y., & Dang, J. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.12.015 · Cited by 46
Evaluating touch-screen vibration modality for blind users to access simple shapes and graphics
Tekli, J., Issa, Y. B., & Chbeir, R. (2017) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2017.10.009 · Cited by 46
Malik, F., & Baharudin, B. (2013) · Cited by 45
Varish, N., & Pal, A. K. (2018) · Applied Intelligence · doi:10.1007/s10489-017-1125-7 · Cited by 45
Automatic textile image annotation by predicting emotional concepts from visual features
Shin, Y., Kim, Y., & Kim, E. Y. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.08.009 · Cited by 44
Ranking consistency for image matching and object retrieval
Chen, Y., Li, X., Dick, A., & Hill, R. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.09.011 · Cited by 44
“In this paper, we focus on local features that are used to compute a BoW vector for each image, whose similarity can then be measured by a dot-product similarity [1,5], set similarity [26] or a liner-SVM classifier [8].”
MethodImproving bag-of-visual-words image retrieval with predictive clustering trees
Dimitrovski, I., Kocev, D., Loškovska, S., & Džeroski, S. (2015) · Information Sciences · doi:10.1016/j.ins.2015.05.012 · Cited by 43
“The methods for image retrieval can be categorized into two categories [16]: text-based image retrieval (TBIR) and content-based image retrieval (CBIR).”
MethodA study in facial features saliency in face recognition: an analytic hierarchy process approach
Karczmarek, P., Pedrycz, W., Kiersztyn, A., & Rutka, P. (2016) · Soft Computing · doi:10.1007/s00500-016-2305-9 · Cited by 43
Rana, S. P., Dey, M., & Siarry, P. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.11.015 · Cited by 43
Multi-channel local ternary pattern for content-based image retrieval
Agarwal, M., Singhal, A., & Lall, B. (2019) · Pattern Analysis and Applications · doi:10.1007/s10044-019-00787-2 · Cited by 43
State of the art content based image retrieval techniques using deep learning: a survey
Kapoor, R., Sharma, D., & Gulati, T. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11045-1 · Cited by 43
“[34] surveyed the distinct facets of research in this area like the use of object ontology to define high level concepts, the use of machine learning association methods, the introduction of relevance feedback in retrieval loop, generation of semantic templates and making use of text information.”
BackgroundAn efficient image retrieval based on HSV color space
Su, C.-H., Chiu, H.-S., & Hsieh, T. (2011) · International Conference on Electrical and Control Engineering · doi:10.1109/iceceng.2011.6058026 · Cited by 43
“In conventional schemes[3][4][5], they extract the image feature vector from images employ descriptors like color histogram, edge histogram and use such as Euclidean distance, Histogram Intersection and Minkowski Metric (LM norm) to measure the similarity of image for matching between a query image and image from data…”
MethodAn endmember-based distance for content based hyperspectral image retrieval
Graña, M., & Veganzones, M. A. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.03.015 · Cited by 42
“The acknowledgment of the semantic gap in general CBIR approaches [12,29,43] has diverted significant efforts in the CBIR literature into two avenues.”
BackgroundSemantic gap in CBIR: automatic objects spatial relationships semantic extraction and representation
Hui, H. W., Mohamad, D., & Ismail, N. A. (2010) · Cited by 42
Mining Statistical Association Rules to Select the Most Relevant Medical Image Features
Ribeiro, M. X., Balan, A. G. R., Felipe, J. C., Traina, A. J. M., & Traina, C. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-88067-7_7 · Cited by 42
“Therefore, content-based image retrieval (CBIR) techniques have been intensively investigated in the last years [16].”
MethodContent-based Image Retrieval by Exploring Bandletized Regions through Support Vector Machines.
Ashraf, R., Bajwa, K. B., & Mahmood, T. (2016) · Journal of information science and engineering · Cited by 41
Khandwala, K., & Guo, P. J. (2018) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3231644.3231652 · Cited by 41
“In the broader computer vision literature, there is ongoing work in both content-based image retrieval [27] and content-based video search [45], which aim to automatically extract semantic meaning from arbitrary images and videos, respectively.”
BackgroundBridging the Semantic Gap via Functional Brain Imaging
Hu, X., Li, K., Han, J., Hua, X., Guo, L., & Liu, T. (2011) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2011.2172201 · Cited by 42
“has been shifted from designing sophisticated low-level feature extraction algorithms to narrowing down the semantic gaps [1], [2].”
BackgroundApplication Potential of Multimedia Information Retrieval
Kankanhalli, M., & Rui, Y. (2008) · Proceedings of the IEEE · doi:10.1109/jproc.2008.916383 · Cited by 40
“A very recent survey on content-based image retrieval with a focus on high-level semantics is [29].”
BackgroundA correlation graph approach for unsupervised manifold learning in image retrieval tasks
Pedronette, D. C. G., & Torres, R. D. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.03.081 · Cited by 40
“More recently, aiming at improving the retrieval effectiveness by reducing the semantic gap, research initiatives have focused on other stages of the retrieval process, which are not directly related to low-level feature extraction procedures [8].”
BackgroundA Survey on Content Based Image Retrieval
Rajam, I. F., Valli, S., & Nagar, J. (2013) · Cited by 40
Ali, H. M. M., & Perveen, S. (2020) · Physiology and Molecular Biology of Plants · doi:10.1007/s12298-020-00831-0 · Cited by 40
A multimedia ontology model based on linguistic properties and audio-visual features
Rinaldi, A. M. (2014) · Information Sciences · doi:10.1016/j.ins.2014.02.017 · Cited by 39
“Moreover, interesting surveys on low-level features and high-level semantics for multimedia analysis and retrieval are respectively in [46,37].”
BackgroundAn integrated approach to Content Based Image Retrieval
Choudhary, R., Raina, N., Chaudhary, N., Chauhan, R., & Goudar, R. H. (2014) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2014.6968394 · Cited by 39
A Framework for Ocean Satellite Image Classification Based on Ontologies
Almendros-Jiménez, J. M., Domene, L. M., & Piedra-Fernandez, J. A. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2217479 · Cited by 39
“, big images, large image archives, distributed image repositories, etc, has motivated the research in the field of Image Retrieval (IR) [1], [2].”
BackgroundQuantifying tag representativeness of visual content of social images
Sun, A., & Bhowmick, S. S. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874029 · Cited by 38
“To partially verify our labeling, we collected the category labels/tags used as visual concepts in earlier work [3, 8, 12, 14] for image classification, annotation, and other tasks.”
BackgroundMethodTowards ontologies for image interpretation and annotation
Bannour, H., & Hudelot, C. (2011) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2011.5972547 · Cited by 45
“In [16], the authors identified five categories of techniques that provide a semantic description of images.”
BackgroundSpatially weighted order binary pattern for color texture classification
Song, T., Feng, J., Wang, S., & Xie, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113167 · Cited by 38
Region of interest-based image retrieval techniques: a review
Jan, M. M., Zainal, N., & Jamaludin, S. (2020) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v9.i3.pp520-528 · Cited by 38
“There have been extensive studies done on CBIR and the progress has been discussed comprehensively in [11-13].”
BackgroundGoyal, A., & Walia, E. (2012) · Signal Image and Video Processing · doi:10.1007/s11760-012-0353-x · Cited by 38
“High level semantics is added to low-level features to improve its working [7].”
BackgroundA medical image retrieval framework in correlation enhanced visual concept feature space
Rahman, M. M., Antani, S., & Thoma, G. R. (2009) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2009.5255392 · Cited by 38
“Further, search results may be improved by combining text attribute-based 1http://www.rsna.org/Technology/DICOM/index.cfm search capability with low-level visual features computed directly on the image content commonly known as ContentBased Image Retrieval (CBIR) [3].”
BackgroundMethodInfluentialVishraj, R., Gupta, S., & Singh, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108450 · Cited by 37
Clinical applications of graph neural networks in computational histopathology: A review
Meng, X., & Zou, T. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107201 · Cited by 42
Peak Valley Edge Patterns: A New Descriptor for Biomedical Image Indexing and Retrieval
Murala, S., & Wu, Q. M. J. (2013) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2013.73 · Cited by 37
“Texture based medical image retrieval is a branch of texture analysis particularly well suited for identification of disease region, and then retrieval of related documents in the database is making it a star of attraction from medical prospective.”
BackgroundCounter-forensics of SIFT-based copy-move detection by means of keypoint classification
Amerini, I., Barni, M., Caldelli, R., & Costanzo, A. (2013) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2013-18 · Cited by 37
“[14–16] focused on a SIFT-based Content Based Image Retrieval (CBIR) [17] scenario and devised a number of interesting attacks.”
MethodInfluentialSemantic-based information retrieval in support of concept design
Setchi, R., Tang, Q., & Stankov, I. S. (2010) · Advanced Engineering Informatics · doi:10.1016/j.aei.2010.07.006 · Cited by 37
Image retrieval based on non-uniform bins of color histogram and dual tree complex wavelet transform
Varish, N., Pradhan, J., & Pal, A. K. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3882-4 · Cited by 37
Image retrieval based on effective feature extraction and diffusion process
Zhou, J., Liu, X., Liu, W., & Gan, J. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6192-1 · Cited by 37
Baig, F., Mehmood, Z., Rashid, M., Javid, M. A., Rehman, A. U., Saba, T., & Adnan, A. (2019) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-019-00237-z · Cited by 37
“Liu et al. (2007) propose a survey to highlight the recent advancements and issues of CBIR.”
BackgroundBuilding and using fuzzy multimedia ontologies for semantic image annotation
Bannour, H., & Hudelot, C. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1491-z · Cited by 39
“However, these approaches face the scalability problem when dealing with broad content image databases [31], i.e. their performances decrease significantly when the concept number is high and depend on the targeted datasets as well [22].”
BackgroundIn Search of Design Inspiration: A Semantic-Based Approach
Setchi, R., & Bouchard, C. (2010) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.3482061 · Cited by 36
“However, keywords belonging to the second category are unlikely to be automatically obtained from images (cid:1) 24,25 (cid:2) because from the point of view of feature representation, there is no single visual feature, which best describes the image content (cid:1) 21 (cid:2) .”
BackgroundMultilabel Neighborhood Propagation for Region-Based Image Retrieval
Li, F., Dai, Q., Xu, W., & Er, G. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.2004914 · Cited by 36
“As it can deal with the variable-length representations, EMD has been introduced into RBIR as an effective distance measure [13], [35], [36], where the set of region importance weights of an image is actually treated as a distribution.”
BackgroundTekli, J. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-021-01650-9 · Cited by 35
“survey studies in [52, 103, 122, 133]) and can be roughly categorized as text-based, contentbased, and hybrid methods.”
BackgroundInfluentialFisher Linear Discriminant Analysis for text-image combination in multimedia information retrieval
Moulin, C., Largeron, C., Ducottet, C., Géry, M., & Barat, C. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.06.003 · Cited by 35
“Other retrieval approaches exploiting media content, called content-based approaches, have been actively studied to develop better image, video or sound/speech search engines as presented in several recent surveys [1, 4, 5, 6].”
BackgroundLoménie, N., & Racoceanu, D. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.01.021 · Cited by 35
“Keywords: Shape analysis, mesh analysis, unorganized point set, spatial relation modeling, mathematical morphological operator, image exploration, graph representation, semantic query, visual reasoning, digital histopathology.”
Content-Based Image Retrieval Using Invariant Color and Texture Features
Afifi, A. J., & Ashour, W. M. (2012) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2012.6411665 · Cited by 35
“Precision is defined as the ratio of the number of retrieved relevant images to the total number of retrieved images [26].”
BackgroundSelf organizing natural scene image retrieval
Serrano-Talamantes, J. F., Avilés‐Cruz, C., Villegas-Cortéz, J., & Sossa, H. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.10.064 · Cited by 35
“Years of research have demonstrated that it is very difficult to achieve satisfactory retrieval results using only one of these feature types (Datta et al., 2008; Liu et al., 2007).”
BackgroundMethodInfluentialApproximate similarity search for online multimedia services on distributed CPU–GPU platforms
Teodoro, G., Junior, E. A. D. V., Mariano, N., Torres, R. D. S., Meira, W., & Saltz, J. H. (2013) · The VLDB Journal · doi:10.1007/s00778-013-0329-7 · Cited by 34
“In one way or another, it remains a critical component, if the system is to be used in real-world, large-scale databases [32].”
BackgroundChaira, T. (2015) · CRC Press eBooks · doi:10.1201/b18019 · Cited by 49
Automatic Auroral Detection in Color All-Sky Camera Images
Rao, J., Partamies, N., Amariutei, O. A., Syrjäsuo, M., & van de Sande, K. E. A. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2321433 · Cited by 34
Alfanindya, A., Hashim, N., & Eswaran, C. (2013) · IEEE International Conference on Technology, Informatics, Management, Engineering and Environment (TIME-E) · doi:10.1109/time-e.2013.6611968 · Cited by 34
“Such works made use of many methods: using object ontology to define high level concept, using machine learning methods to associate low level features with query concepts, using relevance feedback to learn user's intention, generating semantic template to support high level image retrieval, and fusing the evidences f…”
MethodExpert content-based image retrieval system using robust local patterns
Murala, S., & Wu, Q. M. J. (2014) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2014.05.008 · Cited by 34
Query processing issues in region-based image databases
Ilaria Bartolini, A., Ciaccia, P., & Patella, M. (2009) · Knowledge and Information Systems · doi:10.1007/s10115-009-0257-4 · Cited by 34
“Albeit this search paradigm has received some criticism, mainly because it is unlikely that the computer-provided similarity criterion is consistent with the (subjective) user-perceived similarity (this is usually called the semantic gap), content-based retrieval still maintains an important role [26,27].”
BackgroundInfluentialContent-based image retrieval via a hierarchical-local-feature extraction scheme
Jian, M., Yin, Y., Dong, J., & Lam, K. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6122-2 · Cited by 34
“Content-based image retrieval uses the image content itself, such as color, shape and texture features, instead of using the manual labels of images as the search index [17, 25].”
Content based image retrieval by ensembles of deep learning object classifiers
Hamreras, S., Boucheham, B., Molina‐Cabello, M. A., Benítez-Rochel, R., & López‐Rubio, E. (2020) · Integrated Computer-Aided Engineering · doi:10.3233/ica-200625 · Cited by 34
“Content Based Image Retrieval (CBIR) is the procedure of automatically identifying images by the extraction of their low-level visual features like color, texture, shape properties or any other features being derived from the image itself [1].”
An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases
Kashif, M. I., Raja, G., & Shaukat, F. (2020) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-020-00338-w · Cited by 34
“Existing CBIR systems mostly perform image retrieval using three fundamental units namely low-level features, similarity measure, and semantic gap reduction [6].”
MethodReview on: Twin Support Vector Machines
Tian, Y., & Qi, Z. (2014) · Annals of Data Science · doi:10.1007/s40745-014-0018-4 · Cited by 34
“[8–20].”
Ontology driven content based image retrieval
Popescu, A., Millet, C., & Moëllic, P.-A. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282338 · Cited by 34
“The use of ontologies to define high-level concept properties is proposed to ameliorate the results of CBIR systems [ 6 ].”
BackgroundMethodInfluentialRashno, A., Sadri, S., & SadeghianNejad, H. (2015) · CSI International Symposium on Artificial Intelligence and Signal Processing · doi:10.1109/aisp.2015.7123522 · Cited by 33
Latent topics-based relevance feedback for video retrieval
Fernández-Beltrán, R., & Pla, F. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.09.007 · Cited by 33
“As a result, the CBVR field has become a very important research area and a wide variety of CBVR systems have been developed [1, 2, 3, 4].”
BackgroundAhmad, J., Muhammad, K., Bakshi, S., & Baik, S. W. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.11.002 · Cited by 33
“Content-based image retrieval has been extensively investigated by the multimedia research community for more than two decades [16, 17].”
BackgroundA Survey of Feature Extraction for Content-Based Image Retrieval System
Ghosh, N., Agrawal, S., & Motwani, M. (2018) · Lecture notes in networks and systems · doi:10.1007/978-981-10-8198-9_32 · Cited by 33
Learning 3D Semantic Scene Graphs with Instance Embeddings
Wald, J., Navab, N., & Tombari, F. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01546-9 · Cited by 33
“The effectiveness of scene graphs has been demonstrated when solving different scene understanding tasks including image retrieval (Liu et al. 2007; Johnson et al. 2015), scene captioning (Yang et al. 2019), visual question answering (Teney et al. 2017) or image generation from graphs alone…”
BackgroundA Spectral/Spatial CBIR System for Hyperspectral Images
Veganzones, M. A., & Graña, M. (2012) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2012.2186629 · Cited by 33
“A CONTENT-BASED image retrieval (CBIR) system [1]–[3] is able to retrieve the images stored in an image database using as image indexing values feature vectors extracted from the images by means of computer vision and digital image-processing techniques.”
MethodContent Based Image Retrieval System Using CNN based Deep Learning Models
Gautam, G., & Khanna, A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.04.296 · Cited by 32
A knowledge-based multi-layered image annotation system
Ivašić-Kos, M., Ipšić, I., & Ribarič, S. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.07.068 · Cited by 32
Manifold-ranking based retrieval using k-regular nearest neighbor graph
Wang, B., Pan, F., Hu, K., & Paul, J. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.09.006 · Cited by 32
“Learning-based algorithms have become the mainstream in image, 3D model and other multimedia retrieval technologies [1], and they aim at solving the well known ‘‘semantic gap’’ between low-level feature and high-level semantic concept.”
BackgroundLearning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images
Zegarra, J. A. M., Papa, J. P., Leite, N. J., Torres, R. D. S., & Falcão, A. X. (2008) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2008/691924 · Cited by 32
“…Article Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images Javier A. Montoya-Zegarra,1, 2 João Paulo Papa,2 Neucimar J. Leite,2 Ricardo da Silva Torres,2 and Alexandre X. Falcão2 1 Computer Engineering Department, Faculty of…”
BackgroundContent-Based Image Retrieval using Local Patterns and Supervised Machine Learning Techniques
Alrahhal, M., & Supreethi, K. P. (2019) · Amity International Conference on Artificial Intelligence (AICAI) · doi:10.1109/aicai.2019.8701255 · Cited by 32
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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