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Artykuły w czasopismach na temat „Relevance feedback”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Raiber, Fiana, and Oren Kurland. "Relevance Feedback." ACM Transactions on Information Systems 37, no. 4 (2019): 1–28. http://dx.doi.org/10.1145/3360487.

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2

Giorgi, D., P. Frosini, M. Spagnuolo, and B. Falcidieno. "3D relevance feedback via multilevel relevance judgements." Visual Computer 26, no. 10 (2010): 1321–38. http://dx.doi.org/10.1007/s00371-010-0524-0.

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3

Zhou, Dong, Mark Truran, Jianxun Liu, and Sanrong Zhang. "Collaborative pseudo-relevance feedback." Expert Systems with Applications 40, no. 17 (2013): 6805–12. http://dx.doi.org/10.1016/j.eswa.2013.06.030.

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Premkumar, M., and R. Sowmya. "Interactive Content Based Image Retrieval using Multiuser Feedback." JOIV : International Journal on Informatics Visualization 1, no. 4 (2017): 165. http://dx.doi.org/10.30630/joiv.1.4.57.

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Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique wa

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5

Chen, Zhaofeng, Naixuan Guo, Jiu Sun, et al. "Pseudo-Relevance Feedback Method Based on the Topic Relevance Model." Mathematical Problems in Engineering 2022 (July 7, 2022): 1–6. http://dx.doi.org/10.1155/2022/1697950.

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In the field of information retrieval, most pseudo-relevance feedback models select candidate terms from the top k documents returned by the first-pass retrieval, but they cannot identify the reliability of these documents. This paper proposed a new approach to obtain feedback information more comprehensively by constructing four corresponding models. Firstly, the algorithm incorporated topic-based relevance information into the relevance model RM3 and constructed a topic-based relevance model, denoted as TopRM3, with two corresponding variants. TopRM3 estimated the reliability of a feedback d

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6

Panyr, Jiri. "Conceptual Clustering and Relevance Feedback." KNOWLEDGE ORGANIZATION 14, no. 3 (1987): 133–37. http://dx.doi.org/10.5771/0943-7444-1987-3-133.

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7

Crouch, Carolyn J. "Relevance feedback at INEX 2005." ACM SIGIR Forum 40, no. 1 (2006): 58–59. http://dx.doi.org/10.1145/1147197.1147208.

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8

MR., SHINDE SURESH GOROBA, and C. M. JADHAV PROF. "MINING USER NAVIGATION PATTERNS FOR EFFICIENT RELEVANCE FEEDBACK FOR CBIR." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 2 (2018): 4–6. https://doi.org/10.5281/zenodo.1464589.

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 In todayâ€™s modernized world, content based image retrieval (CBIR) is considered as a bastion in image retrieval system. For making CBIR most suitable and productive technique, relevance feedback technique is used in conjunction with CBIR for producing more specific results which are obtained by taking feedback from user. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks for production of best search results, particularly in huge database. But this seems of no use in real world applications. In this paper, we propose

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9

Widyanto, M. Rahmat, and Tatik Maftukhah. "Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 1 (2010): 34–38. http://dx.doi.org/10.20965/jaciii.2010.p0034.

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Fuzzy relevance feedback using Query Vector Modification (QVM) method in image retrieval is proposed. For feedback, the proposed six relevance levels are: “very relevant”, “relevant”, “few relevant”, “vague”, “not relevant”, and “very non relevant”. For computation of user feedback result, QVM method is proposed. The QVM method repeatedly reformulates the query vector through user feedback. The system derives the image similarity by computing the Euclidean distance, and computation of color parameter value by Red, Green, and Blue (RGB) color model. Five steps for fuzzy relevance feedback are:

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10

Algarni, Abdulmohsen, Yuefeng Li, Sheng-Tang Wu, and Yue Xu. "Text mining in negative relevance feedback." Web Intelligence and Agent Systems: An International Journal 10, no. 2 (2012): 151–63. http://dx.doi.org/10.3233/wia-2012-0238.

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11

Patil, Pushpa B., and Manesh Kokare. "Semantic Image Retrieval Using Relevance Feedback." International journal of Web & Semantic Technology 2, no. 4 (2011): 139–48. http://dx.doi.org/10.5121/ijwest.2011.2411.

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12

Tao, Dacheng, Xuelong Li, and Stephen J. Maybank. "Negative Samples Analysis in Relevance Feedback." IEEE Transactions on Knowledge and Data Engineering 19, no. 4 (2007): 568–80. http://dx.doi.org/10.1109/tkde.2007.1003.

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13

Parapar, Javier, Manuel A. Presedo-Quindimil, and Álvaro Barreiro. "Score distributions for Pseudo Relevance Feedback." Information Sciences 273 (July 2014): 171–81. http://dx.doi.org/10.1016/j.ins.2014.03.034.

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14

Lee, Chu-Hui, and Meng-Feng Lin. "Ego-similarity measurement for relevance feedback." Expert Systems with Applications 37, no. 1 (2010): 871–77. http://dx.doi.org/10.1016/j.eswa.2009.05.101.

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15

Joshi, Sandeep, and Satpal Singh Kushwaha. "Query Expansion using Artificial Relevance Feedback." International Journal of Computer Applications 44, no. 7 (2012): 41–45. http://dx.doi.org/10.5120/6279-8448.

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16

Salton, Gerard, and Chris Buckley. "Improving retrieval performance by relevance feedback." Journal of the American Society for Information Science 41, no. 4 (1990): 288–97. http://dx.doi.org/10.1002/(sici)1097-4571(199006)41:4<288::aid-asi8>3.0.co;2-h.

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17

Balakrishnan, Vimala, Kian Ahmadi, and Sri Devi Ravana. "Improving retrieval relevance using users’ explicit feedback." Aslib Journal of Information Management 68, no. 1 (2015): 76–98. http://dx.doi.org/10.1108/ajim-07-2015-0106.

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Purpose – The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback. Design/methodology/approach – CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is further enhanced using case-based reasoning in retrieving the top-5 results. A search engine prototype was developed using Text REtrieval Conference as the document collection, and results were evaluated at three levels (i.e. top-5, 10 and 15). A user evaluation involving 28 students was administered, f

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18

Picariello, Antonio, and Antonio M. Rinaldi. "User Relevance Feedback in Semantic Information Retrieval." International Journal of Intelligent Information Technologies 3, no. 2 (2007): 36–50. http://dx.doi.org/10.4018/jiit.2007040103.

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19

Hamdy, A. "REGION-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK." JES. Journal of Engineering Sciences 40, no. 3 (2012): 819–32. http://dx.doi.org/10.21608/jesaun.2012.114412.

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20

Jing, F., M. Li, H. J. Zhang, and B. Zhang. "Relevance Feedback in Region-Based Image Retrieval." IEEE Transactions on Circuits and Systems for Video Technology 14, no. 5 (2004): 672–81. http://dx.doi.org/10.1109/tcsvt.2004.826775.

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21

Crouch, Carolyn. "Relevance feedback at the INEX 2004 workshop." ACM SIGIR Forum 39, no. 1 (2005): 41–42. http://dx.doi.org/10.1145/1067268.1067282.

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22

Sakai, Tetsuya, Toshihiko Manabe, and Makoto Koyama. "Flexible pseudo-relevance feedback via selective sampling." ACM Transactions on Asian Language Information Processing 4, no. 2 (2005): 111–35. http://dx.doi.org/10.1145/1105696.1105699.

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23

Melucci, Massimo. "Relevance Feedback Algorithms Inspired By Quantum Detection." IEEE Transactions on Knowledge and Data Engineering 28, no. 4 (2016): 1022–34. http://dx.doi.org/10.1109/tkde.2015.2507132.

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24

Wan, Chunru, and Mingchun Liu. "Content-based audio retrieval with relevance feedback." Pattern Recognition Letters 27, no. 2 (2006): 85–92. http://dx.doi.org/10.1016/j.patrec.2005.07.005.

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25

Rao, Yunbo, Wei Liu, Bojiang Fan, Jiali Song, and Yang Yang. "A novel relevance feedback method for CBIR." World Wide Web 21, no. 6 (2018): 1505–22. http://dx.doi.org/10.1007/s11280-017-0523-4.

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26

Vechtomova, Olga, and Murat Karamuftuoglu. "Elicitation and use of relevance feedback information." Information Processing & Management 42, no. 1 (2006): 191–206. http://dx.doi.org/10.1016/j.ipm.2004.10.006.

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27

Orengo, Viviane Moreira, and Christian Huyck. "Relevance feedback and cross-language information retrieval." Information Processing & Management 42, no. 5 (2006): 1203–17. http://dx.doi.org/10.1016/j.ipm.2005.12.003.

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28

Franco, Annalisa, and Alessandra Lumini. "Mixture of KL subspaces for relevance feedback." Multimedia Tools and Applications 37, no. 2 (2007): 189–209. http://dx.doi.org/10.1007/s11042-007-0139-2.

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29

Nguyen, Giang P., and Marcel Worring. "Relevance feedback based saliency adaptation in CBIR." Multimedia Systems 10, no. 6 (2005): 499–512. http://dx.doi.org/10.1007/s00530-005-0178-3.

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30

Chen, Lin, Lin Chun, Lin Ziyu, and Zou Quan. "Hybrid pseudo-relevance feedback for microblog retrieval." Journal of Information Science 39, no. 6 (2013): 773–88. http://dx.doi.org/10.1177/0165551513487846.

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31

Cogalmis, Kevser Nur, Oguzhan Sagoglu, and Ahmet Bulut. "AdScope: Search Campaign Scoping Using Relevance Feedback." IEEE Intelligent Systems 32, no. 3 (2017): 14–20. http://dx.doi.org/10.1109/mis.2017.47.

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32

Ruthven, Ian, Mounia Lalmas, and Keith van Rijsbergen. "Incorporating user search behavior into relevance feedback." Journal of the American Society for Information Science and Technology 54, no. 6 (2003): 529–49. http://dx.doi.org/10.1002/asi.10240.

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33

Bartell, Brian T., Garrison W. Cottrell, and Richard K. Belew. "Optimizing similarity using multi-query relevance feedback." Journal of the American Society for Information Science 49, no. 8 (1998): 742–61. http://dx.doi.org/10.1002/(sici)1097-4571(199806)49:8<742::aid-asi8>3.0.co;2-h.

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34

Bartell, Brian T., Garrison W. Cottrell, and Richard K. Belew. "Optimizing similarity using multi‐query relevance feedback." Journal of the American Society for Information Science 49, no. 8 (1998): 742–61. http://dx.doi.org/10.1002/(sici)1097-4571(199806)49:8<742::aid-asi8>3.3.co;2-8.

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35

RUTHVEN, IAN, and MOUNIA LALMAS. "A survey on the use of relevance feedback for information access systems." Knowledge Engineering Review 18, no. 2 (2003): 95–145. http://dx.doi.org/10.1017/s0269888903000638.

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Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the

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36

LI, JING, and YUAN YUAN. "KERNEL GBDA FOR RELEVANCE FEEDBACK IN IMAGE RETRIEVAL." International Journal of Image and Graphics 07, no. 04 (2007): 767–76. http://dx.doi.org/10.1142/s0219467807002908.

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Relevance feedback, as a user-in-the-loop mechanism, has been widely employed to improve the performance of content-based image retrieval. Generally, in a relevance feedback algorithm, two key components are: (1) how to select a subset of effective features from a large-scale feature pool and, (2) correspondingly, how to construct a suitable dissimilarity measure. In previous work, the biased discriminant analysis (BDA) has been proposed to address these two problems during the feedback iterations. However, BDA encounters the so called small samples size problem because it has a lack of traini

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37

Braam, B., K. D. Mitchell, H. A. Koomans, and L. G. Navar. "Relevance of the tubuloglomerular feedback mechanism in pathophysiology." Journal of the American Society of Nephrology 4, no. 6 (1993): 1257–74. http://dx.doi.org/10.1681/asn.v461257.

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The balance between a high filtration rate and high reabsorption rate in the kidney is critical in the maintenance of extracellular fluid volume. One of the mechanisms that maintain this balance is the tubuloglomerular feedback (TGF) mechanism, which operates at the level of the macula densa assessing the load and/or solute concentration coming out of the loop of Henle and controlling this load by adjusting the GFR. This review discusses the potential role of the TGF system with respect to volume homeostasis in various conditions where GFR is maintained, decreased, or increased. In most of the

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38

Mosquera, Pilar, Eduarda Soares, and Filomena Ribeiro. "THE RELEVANCE OF FEEDBACK ENVIRONMENT FOR JOB SATISFACTION." European Journal of Management Studies 23, no. 2 (2018): 85. http://dx.doi.org/10.5455/ejms/288977/2018.

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39

Lu, Wei. "Image Retrieval Based on Contour and Relevance Feedback." Applied Mechanics and Materials 182-183 (June 2012): 1771–75. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1771.

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In this paper an algorithm is proposed to retrieve images based on contour moment invariants of image and relevance feedback. Firstly, the contour of each query image is extracted and its contour moment invariant is computed. Then according to Euclid Distance between the query image and each image in the image database, the most similar images to the query image can be found. Finally, the relevance feedback algorithm based on support vector machine (SVM) is applied to improve retrieval precision. Experimental results show that the algorithm is more accurate and efficient to retrieve images wit

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40

Chen, Zi Long, and Yang Lu. "Improving Relevance Feedback via Using Support Vector Machines." Advanced Materials Research 255-260 (May 2011): 2028–32. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2028.

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Traditional relevance feedback technique could help improve retrieval performance. It usually utilize the most frequent terms in the relevant documents to enrich the user’s initial query. We re-examine this method and find that many expansion terms identified in traditional approaches are indeed unrelated to the query and harmful to the retrieval. This paper introduces a Support Vector Machines Based method to improve the retrieval results. Firstly, the classifier is trained on the feedback documents. Then, we can utilize this classifier to classify the rest of the documents and move relevant

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41

Akuma, Stephen. "Eye Gaze Relevance Feedback Indicators for Information Retrieval." International Journal of Intelligent Systems and Applications 14, no. 1 (2022): 57–65. http://dx.doi.org/10.5815/ijisa.2022.01.05.

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There is a growing interest in the research on interactive information retrieval, particularly in the study of eye gaze-enhanced interaction. Feedback generated from user gaze features is important for developing an interactive information retrieval system. Generating these gaze features have become less difficult with the advancement of the eye tracker system over the years. In this work, eye movement as a source of relevant feedback was examined. A controlled user experiment was carried out and a set of documents were given to users to read before an eye tracker and rate the documents accord

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42

Zhong Minjuan, and Wan Changxuan. "Pseudo-Relevance Feedback Driven for XML Query Expansion." Journal of Convergence Information Technology 5, no. 9 (2010): 146–56. http://dx.doi.org/10.4156/jcit.vol5.issue9.15.

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43

Shui-Li, Zhang, Dong Jun-Tang, and Liu Li-Li. "A Relevance Feedback Algorithm Combining Bayesian and FSRM." Open Cybernetics & Systemics Journal 9, no. 1 (2015): 491–95. http://dx.doi.org/10.2174/1874110x01509010491.

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Hidekazu, Yanagimoto, and Omatu Sigeru. "Interest Extraction Using Relevance Feedback with Kernel Method." IEEJ Transactions on Electronics, Information and Systems 126, no. 3 (2006): 395–400. http://dx.doi.org/10.1541/ieejeiss.126.395.

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Benitez, A. B., M. Beigi, and Shih-Fu Chang. "Using relevance feedback in content-based image metasearch." IEEE Internet Computing 2, no. 4 (1998): 59–69. http://dx.doi.org/10.1109/4236.707692.

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Yazdi, Hadi Sadoghi, Malihe Javidi, and Hamid Reza Pourreza. "SVM-based Relevance Feedback for semantic video retrieval." International Journal of Signal and Imaging Systems Engineering 2, no. 3 (2009): 99. http://dx.doi.org/10.1504/ijsise.2009.033722.

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Ntalianis, Klimis, Anastasios D. Doulamis, Nicolas Tsapatsoulis, and Nikolaos E. Mastorakis. "Social Relevance Feedback Based on Multimedia Content Power." IEEE Transactions on Computational Social Systems 5, no. 1 (2018): 109–17. http://dx.doi.org/10.1109/tcss.2017.2766250.

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Bjelica, Milan. "Unobtrusive relevance feedback for personalized TV program guides." IEEE Transactions on Consumer Electronics 57, no. 2 (2011): 658–63. http://dx.doi.org/10.1109/tce.2011.5955205.

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Eravci, Bahaeddin, and Hakan Ferhatosmanoglu. "Diversity based relevance feedback for time series search." Proceedings of the VLDB Endowment 7, no. 2 (2013): 109–20. http://dx.doi.org/10.14778/2732228.2732230.

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Mosbah, Mawloud, and Bachir Boucheham. "Pseudo relevance feedback based on majority voting mechanism." International Journal of Web Science 3, no. 1 (2017): 58. http://dx.doi.org/10.1504/ijws.2017.088688.

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