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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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“In addition, there are cases of websites being set up in response to crises in 2003 [Palen and Liu 2007].”
BackgroundLocal Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval
Murala, S., Maheshwari, R. P., & Raman, B. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2188809 · Cited by 680
“A comprehensive and extensive literature survey on CBIR is presented in [1]–[4].”
BackgroundPatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval
Zhou, W., Newsam, S., Li, C., & Shao, Z. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.01.004 · Cited by 571
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“Image segmentation is useful in many cases as they are representative of spatial relations among various objects [12, 127].”
BackgroundA review on automatic image annotation techniques
Zhang, D., Islam, M. M., & Lu, G. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.05.013 · Cited by 483
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“The performance of a CBIR system mainly depends on these selected features [7].”
BackgroundDeep multiple instance learning for image classification and auto-annotation
Wu, J., Yu, Y., Huang, C., & Yu, K. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7298968 · Cited by 414
“Image annotation aims at producing rich image descriptions at different levels [26].”
BackgroundContent‐Based Image Retrieval and Feature Extraction: A Comprehensive Review
Latif, A., Rasheed, A., Sajid, U., Ahmed, J., Ali, N., Ratyal, N. I., Zafar, B., Dar, S. H., Sajid, M., & Khalil, T. (2019) · Mathematical Problems in Engineering · doi:10.1155/2019/9658350 · Cited by 348
“)e image feature vector can be used as an input for machine learning algorithms through training and test models and it can improve the performance of CBIR [1, 2].”
MethodGenerating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval
Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., & Manning, C. D. (2015) · Workshop on Vision and Language (VL) · doi:10.18653/v1/w15-2812 · Cited by 346
“Rui et al. (1999), Liu et al. (2007), and Siddiquie et al. (2011) provide overviews of the developments of this field over the last twenty years.”
BackgroundImage retrieval based on micro-structure descriptor
Liu, G., Li, Z., Lei Zhang, A., & Xu, Y. D. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.02.003 · Cited by 323
“Content-based image retrieval (CBIR) was then emerging in 1980s [1].”
BackgroundImage retrieval based on multi-texton histogram
Liu, G., Lei Zhang, A., Hou, Y., Li, Z., & Yang, J.-Y. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.02.012 · Cited by 291
“…retrieval based on multi-texton histogram Guang-Hai Liu a, , Lei Zhang b, Ying-Kun Hou d, Zuo-Yong Li c, Jing-Yu Yang c a College of Computer Science and Information Technology, Guangxi Normal University, Guilin 541004, China b Department of Computing, The Hong Kong Polytechnic…”
MethodInfluentialSemantic content-based image retrieval: A comprehensive study
Alzu’bi, A., Amira, A., & Ramzan, N. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.07.012 · Cited by 218
Content Based Image Retrieval using Color and Texture
Singha, M. (2012) · Signal & Image Processing An International Journal · doi:10.5121/sipij.2012.3104 · Cited by 215
“A lot of research work has been carried out on Image Retrieval by many researchers, expanding in both depth and breadth [1]-[5].”
Visual diversification of image search results
van Leuken, R. H., Garcia, L., Olivares, X., & van Zwol, R. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526756 · Cited by 214
“Moving on from simple low-level features to more discriminative descriptions, the field has come a long way in narrowing down the semantic gap by using high-level semantics [8].”
Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval
Murala, S., & Wu, Q. M. J. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2288522 · Cited by 213
Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking
Subrahmanyam, M. V. V. S., Maheshwari, R. P., & Raman, B. (2011) · Signal Processing · doi:10.1016/j.sigpro.2011.12.005 · Cited by 200
“Comprehensive and extensive literature survey on CBIR is presented in [1–4].”
BackgroundRECENT ADVANCES ON SUPPORT VECTOR MACHINES RESEARCH
Tian, Y., Shi, Y., & Liu, X. (2012) · Technological and Economic Development of Economy · doi:10.3846/20294913.2012.661205 · Cited by 208
“…in many fields ranging from text categorization (Joachims 1999a; Lodhi et al. Cao 2001), information and image retrieval (Druker et al. 2001; Liu et al. 2007; Tian et al. 2000), information security (Mukkamala et al. 2002) and etc. (Adankon, Cheriet 2009; An-cona et al. 2001; Azimi-Sadjadi,…”
BackgroundMethodFeature selection in image analysis: a survey
Bolón‐Canedo, V., & Remeseiro, B. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09750-3 · Cited by 206
“Two different frameworks can be found in the literature (Liu et al. 2007): one is content-based, i.”
BackgroundInfluentialMultichannel Decoded Local Binary Patterns for Content-Based Image Retrieval
Dubey, S. R., Singh, S. K., & Singh, R. K. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2577887 · Cited by 191
Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data
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“A wide range of far more elaborate approaches exist, from color histogram comparison [30, 31] to local feature matching [32].”
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“There are some literatures that survey the most important CBIR systems [7], [8].”
BackgroundJoint embeddings of shapes and images via CNN image purification
Li, Y., Su, H., Qi, C. R., Fish, N., Cohen‐Or, D., & Guibas, L. J. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818071 · Cited by 173
“The difference between these two levels is referred to as the semantic gap [Smeulders et al. 2000], and is the main focus of CBIR methods, as surveyed in [Liu et al. 2007].”
BackgroundMethodDirectional local extrema patterns: a new descriptor for content based image retrieval
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Recent Advance in Content-based Image Retrieval: A Literature SurveyPreprint
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“The progress in those years has been comprehensively discussed in existing survey papers [5] [6] [7].”
BackgroundVerma, M., & Raman, B. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4834-3 · Cited by 153
“1 Motivation Many methods have been proposed for image feature extraction in content based image retrieval [23, 30, 47].”
BackgroundSemantic hierarchies for image annotation: A survey
Tousch, A.-M., Herbin, S., & Audibert, J.-Y. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.05.017 · Cited by 157
Region-based image retrieval with high-level semantics using decision tree learning
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“The state-of-the-art techniques for reducing the 'semantic gap' can be roughly classified into five categories [10].”
BackgroundDynamic Label Propagation for Semi-supervised Multi-class Multi-label Classification
Wang, B., Zhuowen Tu, A., & Tsotsos, J. K. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.60 · Cited by 142
A Survey On: “Content Based Image Retrieval Systems”
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Rashedi, E., Nezamabadi–pour, H., & Saryazdi, S. (2012) · Knowledge-Based Systems · doi:10.1016/j.knosys.2012.10.011 · Cited by 130
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Efficient manifold ranking for image retrieval
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“COREL is widely used in many CBIR works [12, 19, 31].”
BackgroundLocal Diagonal Extrema Pattern: A New and Efficient Feature Descriptor for CT Image Retrieval
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“The information contained in the image can be represented in the form of the feature vectors using efficient image descriptors which can be used in the content based image retrieval system to retrieve the most similar images of a given query image [3][4].”
BackgroundMethodA Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF
Ali, N., Bajwa, K. B., Sablatnig, R., Chatzichristofis, S. A., Iqbal, Z., Rashid, M., & Habib, H. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157428 · Cited by 122
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Malik, F., & Baharudin, B. (2012) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2012.11.004 · Cited by 117
“To overcome the problem of space and manipulation time, at present almost all images are represented in compressed formats like JPEG and MPEG (Liu et al., 2007; Nezamabadi-pour and Saryazdi, 2005).”
BackgroundMethodInfluentialWen, D., Huang, X., Zhang, L., & Benediktsson, J. A. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2463075 · Cited by 116
“, scene classification [47]–[49], content-based image retrieval [50], and complex”
BackgroundDirectional Binary Wavelet Patterns for Biomedical Image Indexing and Retrieval
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Siamese graph convolutional network for content based remote sensing image retrieval
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BackgroundComputer-Aided Diagnosis of Mammographic Masses Using Scalable Image Retrieval
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Sharif, U., Mehmood, Z., Mahmood, T., Javid, M. A., Rehman, A. U., & Saba, T. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-018-9636-0 · Cited by 114
“In the last three decades, different feature extraction techniques have been employed based on appearances such as color, boundary contour, texture, and spatial layout (Mukherjee et al. 2015; Yuan et al. 2011; Liu et al. 2007; Zhang et al. 2012; Krajník et al. 2015; Arampatzis et al. 2013; Leutenegger et al. 2011; Low…”
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On Combining Multiple Features for Cartoon Character Retrieval and Clip Synthesis
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“Another research domain related to cartoon retrieval and synthesis is content-based image retrieval [20], [28], [29], [33].”
BackgroundSemantics for Robotic Mapping, Perception and Interaction: A Survey
Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I. D., Gould, S. J., et al. (2020) · Foundations and Trends in Robotics · doi:10.1561/2300000059 · Cited by 122
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Imran, M., Castillo, C., Díaz, F., & Vieweg, S. E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.7071 · Cited by 100
“In addition, there are cases of websites being set up in response to crises in 2003 [Palen and Liu 2007].”
BackgroundEMR: A Scalable Graph-Based Ranking Model for Content-Based Image Retrieval
Xu, B., Bu, J., Chen, C., Wang, C., Cai, D., & He, X. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.70 · Cited by 97
“COREL is widely used in many CBIR works [2], [41], [42].”
MethodFeature selection based-on genetic algorithm for image annotation
Lu, J., Zhao, T., & Zhang, Y. (2008) · Knowledge-Based Systems · doi:10.1016/j.knosys.2008.03.051 · Cited by 97
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Xia, Z., Jiang, L., Liu, D., Lu, L., & Jeon, B. (2019) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2019.2927215 · Cited by 96
Understanding Digital Image Processing
Tyagi, V. (2018) · CRC Press eBooks · doi:10.1201/9781315123905 · Cited by 92
Jabeen, S., Mehmood, Z., Mahmood, T., Saba, T., Rehman, A. U., & Mahmood, M. T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194526 · Cited by 90
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Assessment of Crohn’s Disease Lesions in Wireless Capsule Endoscopy Images
Kumar, R., Zhao, Q., Seshamani, S., Mullin, G. E., Hager, G. D., & Dassopoulos, T. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2172438 · Cited by 87
“In comparison to other content-based image retrieval [8] and conventional picture archiving and communication systems [9], only a few research systems [10]–[12] have investigated visual medical imaging such as conventional endoscopy, or CE images.”
BackgroundContent-based medical image classification using a new hierarchical merging scheme
Pourghassem, H., & Ghassemian, H. (2008) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2008.07.006 · Cited by 87
“To access images according to their content, low-level features such as colors, textures, and shapes of objects are widely used as indexing features for image retrieval [1].”
Attention-driven salient edge(s) and region(s) extraction with application to CBIR
Feng, S., Xu, D., & Yang, X. (2009) · Signal Processing · doi:10.1016/j.sigpro.2009.05.017 · Cited by 86
Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions
Ashraf, R., Bashir, K., Irtaza, A., & Mahmood, M. T. (2015) · Entropy · doi:10.3390/e17063552 · Cited by 85
“, distance or semantic association by a machine learning technique [15].”
BackgroundMethodRelevance feedback based on genetic programming for image retrieval
Ferreira, C. D., Santos, J. A., Torres, R. D. S., Gonçalves, M. A., Rezende, R. C., & Fan, W. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.05.015 · Cited by 85
“Motivated by these limitations, relevance feed-back (RF) approaches were incorporated into CBIR systems [31,26,5,27,13].”
MethodMehmood, Z., Mahmood, T., & Javid, M. A. (2017) · Applied Intelligence · doi:10.1007/s10489-017-0957-5 · Cited by 84
Real-time object detection and localization with SIFT-based clustering
Piccinini, P., Prati, A., & Cucchiara, R. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.004 · Cited by 84
“…June 2012 Keywords: Pick-and-place applications Machine vision for industrial applications SIFT This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based…”
BackgroundLearning image similarity from Flickr groups using Stochastic Intersection Kernel MAchines
Wang, G., Hoiem, D., & Forsyth, D. A. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459167 · Cited by 84
“The experimental results show our approach performs better on image matching, retrieval, and classification than using conventional visual features.”
MethodHavas, C., Resch, B., Francalanci, C., Pernici, B., Scalia, G., Fernandez-Marquez, J., Achte, T. V., Zeug, G., Mondardini, M., Grandoni, D., et al. (2017) · Italian National Conference on Sensors · doi:10.3390/s17122766 · Cited by 71
“Available algorithms can determine colour, shape and image texture [57].”
BackgroundXie, L., Wang, J., Zhang, B., & Tian, Q. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2408566 · Cited by 81
“A survey on this topic could be found in [68].”
BackgroundRETRACTED ARTICLE: Video analytics for semantic substance extraction using OpenCV in python
Manju, A., & Valarmathie, P. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01780-y · Cited by 81
RetractedNonlinear Deep Kernel Learning for Image Annotation
Jiu, M., & Sahbi, H. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2666038 · Cited by 78
“This framework is closely related to Laplacian SVMs, and its objective function is defined as min w,{ fk} K∑ k=1 Ck ∑ i=1 max ( 0, 1− yki fk(xi ) )+ 1 2 ∥ ∥ fk ∥ ∥2H +λ2 +u∑ i=1 ∑ j∈Ni A(xi , x j )( fk(xi)− fk(x j ))2.”
Medical Image Retrieval: Past and Present
Hwang, K. H., Lee, H., & Choi, D.-J. (2012) · Healthcare Informatics Research · doi:10.4258/hir.2012.18.1.3 · Cited by 77
Towards automated classification of fine-art painting style
Arora, R. (2012) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t3xp73qp · Cited by 82
“in [24] provides a comprehensive study of five major techniques utilizing semantic level features and their applications in CBIR systems.”
PhotoNet: A Similarity-Aware Picture Delivery Service for Situation Awareness
Uddin, M. Y. S., Wang, H., Saremi, F., Qi, G.-J., Abdelzaher, T., & Huang, T. S. (2011) · IEEE Real-Time Systems Symposium (RTSS) · doi:10.1109/rtss.2011.36 · Cited by 76
“We borrow mechanisms from vision literature [ 1 ] to determine visual similarity between communicated images.”
MethodMemon, M. H., Li, J., Memon, I., & Arain, Q. A. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3834-z · Cited by 75
“It observed that, they are going to recognise the objects like video, robot vision, mobile devices and electronics games by using the concept of matching two images, motion texture and much more in [32, 40, 64, 86].”
BackgroundGao, J., & Long, C. (2023) · Proceedings of the ACM on Management of Data · doi:10.1145/3589282 · Cited by 76
“K nearest neighbor (KNN) search in the high-dimensional Euclidean vector space is a fundamental problem and has a wide range of applications in information retrieval [41], data mining [13] and recommendations [53].”
BackgroundA privacy-preserving content-based image retrieval method in cloud environment
Xu, Y., Gong, J., Xiong, L., Xu, Z., Wang, J., & Shi, Y.-Q. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.01.006 · Cited by 74
Li, R., Lu, J., Zhang, Y., & Zhao, T. (2009) · Knowledge-Based Systems · doi:10.1016/j.knosys.2009.11.020 · Cited by 71
An Overview of Perceptual Hashing
Farid, H. (2021) · Journal of Online Trust and Safety · doi:10.54501/jots.v1i1.24 · Cited by 70
“The many choices for each of these stages result in varying compromises between the five desired properties of a hash (see Smeulders et al. 2000; Liu et al. 2007; Du, Ho, and Cong 2020 for extensive surveys of different techniques).”
BackgroundRemote Sensing Image Retrieval by Scene Semantic Matching
Wang, M., & Song, T. (2012) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2012.2217397 · Cited by 70
“SBIR/SBRSIR currently involves the following three main aspects [1], [10], [11]: 1) image semantic representation; 2) image semantic extraction for bridging image low-level VFs to high-level semantics; and 3) semantic matching and utilization.”
BackgroundThe Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval
Asim, M. N., Wasim, M., Khan, M. U. G., Mahmood, N., & Mahmood, W. (2019) · IEEE Access · doi:10.1109/access.2019.2897849 · Cited by 70
“fashion, medicine, publishing, architecture and remote sensing etc [83].”
BackgroundMethodAn Approach to Supporting Incremental Visual Data Classification
Paiva, J. G. S., Schwartz, W. R., Pedrini, H., & Minghim, R. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2331979 · Cited by 68
“Second, users tend to employ high-level concepts to interpret images and measure their similarity, while mostly low-level features are automatically extracted, producing a semantic gap [25].”
Segalin, C., Cheng, D., & Cristani, M. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.10.013 · Cited by 69
Image annotation: Then and now
Bhagat, P. K., & Choudhary, P. (2018) · Image and Vision Computing · doi:10.1016/j.imavis.2018.09.017 · Cited by 66
A generic framework for ontology-based information retrieval and image retrieval in web data
Vijayarajan, V., Dinakaran, M., Tejaswin, P., & Lohani, M. (2016) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-016-0074-1 · Cited by 66
Remote Sensing Image Retrieval in the Past Decade: Achievements, Challenges, and Future Directions
Zhou, W., Guan, H., Li, Z., Shao, Z., & Delavar, M. R. (2023) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2023.3236662 · Cited by 69
“However, the results cannot well reflect the users’ real query intensions due to the “semantic gap” between the visual features and, in particular, the low-level features and the richness of human semantics [209].”
BackgroundDynamic label propagation for semi-supervised multi-class multi-label classification
Wang, B., & Tsotsos, J. K. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.10.006 · Cited by 65
Facial attributes for active authentication on mobile devices
Samangouei, P., Patel, V. M., & Chellappa, R. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.05.004 · Cited by 65
“The first use of attributes as higher order features was introduced in content-basedd image retrieval where they are presented as a solution to decrease the semantic gap [12, 13, 14].”
MethodA review of content-based image retrieval
Rafiee, G., Dlay, S. S., & Woo, W. L. (2010) · International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) · doi:10.1109/csndsp16145.2010.5580313 · Cited by 64
Non-Boolean computing with nanomagnets for computer vision applications
Bhanja, S., Karunaratne, D. K., Panchumarthy, R., Rajaram, S., & Sarkar, S. (2015) · Nature Nanotechnology · doi:10.1038/nnano.2015.245 · Cited by 62
Sarwar, A., Mehmood, Z., Saba, T., Qazi, K. A., Adnan, A., & Jamal, H. (2018) · Journal of Information Science · doi:10.1177/0165551518782825 · Cited by 62
Content-based medical image retrieval system for lung diseases using deep CNNs
Agrawal, S., Chowdhary, A., Agarwala, S., Mayya, V., & Kamath, S. S. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01007-7 · Cited by 64
“The performance of such a CBIR system is highly reliant on chosen features [6], which may result in overfitting when the quantity of input images is inadequate.”
BackgroundJuneja, K., Verma, A., Goel, S., & Goel, S. (2015) · International Conference on Computational Intelligence & Communication Technology (CICT) · doi:10.1109/cict.2015.92 · Cited by 61
“For image retrieving purpose, two frameworks were developed, text-based image retrieval (TBIR) and content-based image retrieval (CBIR) [1].”
MethodImage Retrieval Based on Content Using Color Feature
Afifi, A. J., & Ashour, W. M. (2012) · ISRN Computer Graphics · doi:10.5402/2012/248285 · Cited by 61
“Recall is defined as the ratio of the number of retrieved relevant images to the total number of relevant images in the database [33].”
BackgroundCBIR USING COLOR HISTOGRAM PROCESSING
Suhasini, P. S., & Krishna, I. V. M. (2009) · Cited by 59
4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling
Bahmani, S., Skorokhodov, I., Rong, V., Wetzstein, G., Guibas, L. J., Wonka, P., Tulyakov, S., Park, J. J., Tagliasacchi, A., & Lindell, D. B. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00764 · Cited by 114
“The problem itself is also related to other methods for text-based image retrieval [35] or image-conditioned text generation [62, 78].”
Pedronette, D. C. G., Gonçalves, F. M. F., & Guilherme, I. R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.009 · Cited by 59
“Mainly due to this difficulties, the research focus was gradually shifted from designing low-level features to aspects related to higher level aspects [9].”
A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words
Mehmood, Z., Anwar, S. M., Ali, N., Habib, H. A., & Rashid, M. (2016) · Mathematical Problems in Engineering · doi:10.1155/2016/8217250 · Cited by 58
“The main focus of the research in CBIR is to retrieve the images that are in a semantic relationship with the query image [3, 5].”
BackgroundGlobal Correlation Descriptor: A novel image representation for image retrieval
Feng, L., Wu, J., Liu, S., & Zhang, H. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.09.002 · Cited by 58
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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