Dissertations / Theses on the topic 'Relevance feedback'
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Zhang, Peng. "Approximating true relevance model in relevance feedback." Thesis, Robert Gordon University, 2013. http://hdl.handle.net/10059/808.
Full textXu, Zuobing. "Active relevance feedback algorithms /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2008. http://uclibs.org/PID/11984.
Full textYamout, Fadi. "Relevance Feedback Using Weight Propagation." Thesis, University of Sunderland, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485442.
Full textRuthven, Ian. "Abduction, explanation and relevance feedback." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392605.
Full textKrishnan, Sharenya. "Text-Based Information Retrieval Using Relevance Feedback." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-53603.
Full textVinay, V. "The relevance of feedback for text retrieval." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1446146/.
Full textGusenbauer, Stefan. "Relevance feedback in information retrieval a comparison including a practical evaluation of several approaches to relevance feedback in information retrieval." Saarbrücken VDM Verlag Dr. Müller, 2006. http://d-nb.info/989497259/04.
Full textJin, Lan. "Relevance feedback in the retrieval of reusable software components." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22831.pdf.
Full textDjordjevic, Divna. "User relevance feedback, search and retrieval of visual content." Thesis, Queen Mary, University of London, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.432897.
Full textNg, Chee Un. "Image retrieval using experience-based relevance feedback and visualisation." Thesis, University of Warwick, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425556.
Full textCheng, Sijin. "Relevance feedback-based optimization of search queries for Patents." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154173.
Full textMonti, Eleonora. "Recupero con "relevance feedback" di immagini dermatologiche mediante funzioni filtranti." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14686/.
Full textEfthimiadis, Efthimis Nikolaos. "Interactive query expansion and relevance feedback for document retrieval systems." Thesis, City University London, 1992. http://openaccess.city.ac.uk/7891/.
Full textMan, Chun Him. "Human face image searching system with relevance feedback using sketch." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/618.
Full textLiu, Danzhou. "EFFICIENT TECHNIQUES FOR RELEVANCE FEEDBACK PROCESSING IN CONTENT-BASED IMAGE RETRIEVAL." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2991.
Full textPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD
Wong, Chan Fong. "Content-based image retrieval using color quantization, rectangular segmentation, and relevance feedback." Thesis, University of Macau, 2008. http://umaclib3.umac.mo/record=b1780398.
Full textOuattara, Anatole Kinaya. "La recherche d'informations sur le Web et la méthode du Relevance Feedback." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0007/MQ41973.pdf.
Full textMenard, Jr Kevin Joseph. "Evaluating User Feedback Systems." Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-theses/702.
Full textMarques, Joselene. ""Realimentação de relevância para recuperação por conteúdo de imagens médicas visando diminuir a descontinuidade semântica"." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18062006-204746/.
Full textThis Master project aimed at studying, analyzing and developing relevance feedback (RF) techniques to enhance similarity queries that employ the content-based image retrieval (CBIR) approach. The motivation to develop this project came from the iRIS (internet Retrieval of Images System), which is a Web server prototype to process similarity queries. The iRIS can be integrated to a PACS (Picture and Archiving and Communication System) adding the functionality of retrieval images comparing their inherent alikeliness. The main reservation about using CBIR techniques is the semantic gap, because the general use of low level features to describe the images. The low level features, such as color, texture and shape, mostly cannot bridge the gap between what the users expect/want to what they get, generating disappointment and refusal of employing the system. However, if the user is allowed to interact with the system, classifying the query results and using such information on refinement steps, the queries can be reprocessed and the results tend to comply with the users expectation. This is just the core of the relevance feedback techniques. Looking at this scenario, this project developed two relevance feedback (RF) techniques: the RF Projection and the RF Multiple Point Projection. The improvements on the similarity queries were expressive going to up 29% with only one interaction, and to 42% on the fifth interaction, when compared to the original query. Experiments performed with users, have shown us that in average they run 3 iterations before get satisfactory results. By the results given by the experiment, one can claim that RF is a powerful approach to improve the use of CBIR systems and enhance similarity queries.
Nordin, Alexandra. "Improving an Information Retrieval System by Using Machine Learning to Improve User Relevance Feedback." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185184.
Full textI detta examensarbetet så är målet att förbättra ett exi- sterande informationssökningssystem som använder sig av relevansåterkoppling för att utföra sökfrågeexpansion. Det finns en konstant efterfrågan att förbättra prestandan av detta system då de dokument som returneras används för olika dataanalysuppgifter. Därför är det viktigt att både precision och täckning är så högt som möjligt. En använ- dare kan välja att ge relevansåterkoppling, vilket betyder att användaren markerar dokument som är relevanta och irrelevanta, vilket sedan används för att utföra sökfråge- expansion. Den initiala sökfrågan expanderas utifrån in- formation från relevansåterkopplingen. Tillvägagångssättet som presenteras i detta arbete använder de markerade do- kumenten för att träna en maskininlärningsmodell som kan klassificera oklassade document som relevanta, irrelevanat eller okända. Målet är att klassificera okända dokument och sedan lägga till dem till uppsättningen av relevansåterkopp- lingsdokument som användaren har markerat. Antagandet som denna metod baseras på är att ju mer relevansåter- koppling som ges, desto bättre sökfrågeexpansion kan sy- stemet utföra. Systemet som utvecklades i detta examens- arbete är byggt för och evaluerat mot data som äs skrivet på engelska. Resultaten i detta arbete visar att denna metod förbättrade resultaten i tre utav fyra testfall. Prestandan för det existerande systemet är redan på en hög nivå, men små förbättringar är viktiga. Det skulle vara en fördel att integrera detta i det existerande systemet.
Eriksen, Martin. "Rocchio, Ide, Okapi och BIM : En komparativ studie av fyra metoder för relevance feedback." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18877.
Full textUppsatsnivå: D
Xu, Xiaoqian. "Shape Matching, Relevance Feedback, and Indexing with Application to Spine X-Ray Image Retrieval." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1648.pdf.
Full textBillerbeck, Bodo, and bodob@cs rmit edu au. "Efficient Query Expansion." RMIT University. Computer Science and Information Technology, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20060825.154852.
Full textZellhöfer, David [Verfasser], and Ingo [Akademischer Betreuer] Schmitt. "A preference-based relevance feedback approach for polyrepresentative multimedia retrieval / David Zellhöfer ; Betreuer: Ingo Schmitt." Cottbus : BTU Cottbus - Senftenberg, 2015. http://d-nb.info/1114283843/34.
Full textDi, Buccio Emanuele. "Design, Implementation and Evaluation of a Methodology for Utilizing Sources of Evidence in Relevance Feedback." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421639.
Full textL'obiettivo di un sistema di reperimento dell'informazione è quello di supportare l'utente in cerca di informazioni predicendo quali documenti siano rilevanti per la sua esigenza informativa. La predizione di rilevanza è effettuata sulla base dell'evidenza disponibile durante il processo di reperimento. Le interazioni che coivolgono l'utente sono esempi di sorgenti di evidenza. Questa tesi affronta il problema della modellazione uniforme di forme eterogenee di interazione utilizzate come sorgenti di retroazione. Il problema della modellazione uniforme delle sorgenti è affrontato mediante l'introduzione di una metodologia, finalizzata alla progettazione, la realizzazione e la valutazione di un sistema per validare ipotesi sperimentali. Le ipotesi riguardano i possibili fattori che possano spiegare la percezione di rilevanza dell'utente sulla base dell'evidenza ottenuta da interazioni che coinvolgano l'utente stesso. L'obiettivo è quello di ottenere una rappresentazione dei fattori che possa essere utilizzata come una nuova dimensione della rappresentazione dell'esigenza informativa. La metodologia si propone di essere generale e non specifica per una particolare sorgente. Essa definisce una serie di passi necessari per ottenere una rappresentazione in termini di sottospazi delle dimensioni della rappresentazione dell'esigenza informativa per poi utilizzare tale rappresentazione al fine della predizione. La tesi applica la metodologia per modellare due sorgenti di evidenza: le relazioni tra i termini nei documenti giudicati rilevanti e la relazione tra attributi utilizzati per caratterizzare il comportamento dell'utente durante l'interazione con i documenti. In merito alla relazione tra i termini questa tesi mostra come la attuale implementazione per questa sorgente possa essere utilizzata per effettuare il reperimento su collezioni molto ampie, in particolare quelle adottate nelle campagne di valutazione dell'iniziativa Text Retrieval Conference, nello specifico nelle track di Relevance Feedback tenutesi nel 2009 e nel 2010. La metodologia ha consentito di supportare la valutazione del riordinamento dei documenti basato sulle relazioni tra i termini. In merito alle relazioni tra attributi per caratterizzare il comportamento dell'utente questa tesi investiga l'utilizzo di una dimensione basata su tale sorgente per effettuare un riordinamento dei documenti sia unicamente basato sul comportamento, sia mediante espansione dell'interrogazione.
Bossard, Elaine Ardis. "Examining the roles of frame, frequency, and relevance in performance feedback: exploring evaluative and behavioral outcomes of decision making." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/2048.
Full textcom, chungkp@yahoo, and Kien Ping Chung. "Intelligent content-based image retrieval framework based on semi-automated learning and historic profiles." Murdoch University, 2007. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20070831.123947.
Full textSmith, Martin Philip. "The effectiveness of document ranking and relevance feedback techniques in a thesaurus-based search intermediary system." Thesis, University of Huddersfield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296001.
Full textPöttker, Luciana Maria Vieira [UNESP]. "Arquitetura para recuperação de objetos de aprendizagem – uma abordagem baseada em agentes inteligentes e relevance feedback." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/150090.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Desde a sua criação, a Web tem crescido a um ritmo sem precedentes, situação esta que exigiu mudanças na forma como a sociedade busca e tem acesso à informação. O incremento informacional foi observado em todas as áreas do conhecimento e, desde então, problemas relacionados à recuperação de informação são investigados. No escopo dessa investigação, são pesquisados objetos de aprendizagem que estejam descritos, em um padrão de metadados educacional, e mantidos em repositórios específicos para este fim. Todos os problemas e dificuldades relacionados à recuperação de informação são refletidos no contexto particular dos objetos de aprendizagem. Devido à natureza (multimídia) dos objetos de aprendizagem, a complexidade em recuperá-los se torna mais perceptível. Nesta tese, propõe-se um modelo de arquitetura para recuperação de objetos de aprendizagem baseado em uma integração de tecnologias de sistemas de recuperação de informação, metadados, relevance feedback e agentes inteligentes. O propósito fundamental da arquitetura para recuperação de objetos de aprendizagem é unificar a representação desses recursos educacionais que são disponibilizados em diferentes repositórios e permitir que o usuário realize buscas qualificadas para localizar os objetos de aprendizagem mais adequados para sua necessidade de informação. Esta pesquisa é classificada como qualitativa e de natureza aplicada, uma vez que se relaciona com o problema prático de recuperação de objetos de aprendizagem disponíveis em repositórios da Web. O principal diferencial desta proposta foi de valorizar a inferência do usuário no processo de recuperação de informação, por meio do processo de relevance feedback. Neste processo, o usuário estabelece um diálogo com o sistema de recuperação de informação realizando refinamentos nos resultados que lhe foram retornados. Como esse processo é cíclico, ele pode ser executado até que o usuário esteja satisfeito com os resultados que lhe foram retornados. Conclui-se que um sistema de recuperação de informação é mais eficiente quando amplia seu escopo de recuperação a partir de diferentes fontes de dados e permite a inferência do usuário no julgamento da informação que lhe foi retornada.
The Web has been growing in a record speed since its creation and, therefore, such prospect has demanded changes in the way society seeks for and accesses information. Informational increment was evident in all fields of knowledge and since then, the relevant information retrieval issues have been investigated. In the scope of this investigation, we find researches in learning objects classified into an educational metadata pattern and kept in a specific repository. All the problems and complications related to such retrieval reflect in the learning objects particular context. The complexity in retrieving these learning objects becomes evident given their (multimedia) nature. Here, we suggest an architecture model to retrieve the aforementioned objects that is based on a combination of information retrieval system, metadata, relevance feedback, and intelligent agents. The main purpose of this architecture model is to unify the representation of these educational resources – that are available in a heterogeneous repository – and allow users to perform efficient searches in order to find the most suitable learning objects to their information needs. This is a qualitative and Applied research once it relates to the practical problem of learning objects retrieval available on the Web. The main difference of this suggestion was to value – via relevance feedback – the importance of the user‟s inference in the process of such retrieval, in which the user establishes a dialog with the information retrieval system as to enhance the obtained results, and thus – being a cyclical process – it can be executed until he is pleased them. The conclusion is that an information retrieval system more efficient when its scope is enlarged from the different sources of data and allows the inference of the user when judging what he was presented with.
Pöttker, Luciana Maria Vieira. "Arquitetura para recuperação de objetos de aprendizagem - uma abordagem baseada em agentes inteligentes e relevance feedback /." Marília, 2017. http://hdl.handle.net/11449/150090.
Full textBanca: Elvis Fusco
Banca: Walter Moreira
Banca: Ana Carolina Simionato
Banca: Rachel Cristina Vesu Alves
Resumo: Desde a sua criação, a Web tem crescido a um ritmo sem precedentes, situação esta que exigiu mudanças na forma como a sociedade busca e tem acesso à informação. O incremento informacional foi observado em todas as áreas do conhecimento e, desde então, problemas relacionados à recuperação de informação são investigados. No escopo dessa investigação, são pesquisados objetos de aprendizagem que estejam descritos, em um padrão de metadados educacional, e mantidos em repositórios específicos para este fim. Todos os problemas e dificuldades relacionados à recuperação de informação são refletidos no contexto particular dos objetos de aprendizagem. Devido à natureza (multimídia) dos objetos de aprendizagem, a complexidade em recuperá-los se torna mais perceptível. Nesta tese, propõe-se um modelo de arquitetura para recuperação de objetos de aprendizagem baseado em uma integração de tecnologias de sistemas de recuperação de informação, metadados, relevance feedback e agentes inteligentes. O propósito fundamental da arquitetura para recuperação de objetos de aprendizagem é unificar a representação desses recursos educacionais que são disponibilizados em diferentes repositórios e permitir que o usuário realize buscas qualificadas para localizar os objetos de aprendizagem mais adequados para sua necessidade de informação. Esta pesquisa é classificada como qualitativa e de natureza aplicada, uma vez que se relaciona com o problema prático de recuperação de objetos de aprendizagem disponív... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: The Web has been growing in a record speed since its creation and, therefore, such prospect has demanded changes in the way society seeks for and accesses information. Informational increment was evident in all fields of knowledge and since then, the relevant information retrieval issues have been investigated. In the scope of this investigation, we find researches in learning objects classified into an educational metadata pattern and kept in a specific repository. All the problems and complications related to such retrieval reflect in the learning objects particular context. The complexity in retrieving these learning objects becomes evident given their (multimedia) nature. Here, we suggest an architecture model to retrieve the aforementioned objects that is based on a combination of information retrieval system, metadata, relevance feedback, and intelligent agents. The main purpose of this architecture model is to unify the representation of these educational resources - that are available in a heterogeneous repository - and allow users to perform efficient searches in order to find the most suitable learning objects to their information needs. This is a qualitative and applied research once it relates to the practical problem of learning objects retrieval available on the Web. The main difference of this suggestion was to value - via relevance feedback - the importance of the user‟s inference in the process of such retrieval, in which the user establishes a dialog with the information retrieval system as to enhance the obtained results, and thus - being a cyclical process - it can be executed until he is pleased them. The conclusion is that an information retrieval system more efficient when its scope is enlarged from the different sources of data and allows the inference of the user when judging what he was presented with.
Doutor
Ekberg-Selander, Karin, and Johanna Enberg. "Query Expansion : en jämförande studie av Automatisk Query Expansion med och utan relevans-feedback." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-18416.
Full textUppsatsnivå: D
Lipani, Aldo. "Query rewriting in information retrieval: automatic context extraction from local user documents to improve query results." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4528/.
Full textBot, Razvan Stefan. "Improving document representation by accumulating relevance feedback the relevance feedback accumulation (RFA) algorithm /." Thesis, 2005. http://library1.njit.edu/etd/fromwebvoyage.cfm?id=njit-etd2005-127.
Full text"World-wide web information discovery via relevance feedback." 1998. http://library.cuhk.edu.hk/record=b5889686.
Full textThesis (M.Phil.)--Chinese University of Hong Kong, 1998.
Includes bibliographical references (leaves 100-106).
Abstract also in Chinese.
Abstract --- p.i
Abstract (Chinese) --- p.iv
Acknowledgement --- p.vi
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- The World-Wide Web --- p.1
Chapter 1.2 --- Searching Information on the WWW --- p.2
Chapter 1.3 --- Intelligent content-based information discovery on the Web --- p.4
Chapter 1.4 --- Organization of the Thesis --- p.7
Chapter 2 --- Literature Review --- p.9
Chapter 2.1 --- Search Engines --- p.9
Chapter 2.2 --- Information Indexing Systems --- p.11
Chapter 2.3 --- Agent-based Systems --- p.13
Chapter 2.4 --- Information Filtering Systems --- p.16
Chapter 3 --- Overview of the Proposed Approach --- p.20
Chapter 3.1 --- System Architecture --- p.21
Chapter 3.2 --- Topic Profile Specification --- p.25
Chapter 3.3 --- Text Representation --- p.29
Chapter 3.3.1 --- Profile Feature Representation --- p.30
Chapter 3.3.2 --- Document Feature Representation --- p.33
Chapter 3.4 --- Advantages of the Topic Profile Specifications --- p.34
Chapter 4 --- Relevance Score Evaluation Process and Relevance Feedback Model --- p.36
Chapter 4.1 --- Term Weights --- p.37
Chapter 4.2 --- Document Evaluation through Relevance Score --- p.39
Chapter 4.3 --- Learning via Relevance Feedback --- p.42
Chapter 4.3.1 --- Introduction to Relevance Feedback --- p.43
Chapter 4.3.2 --- Feature Extraction from the Relevance Feedback Models --- p.44
Chapter 4.3.3 --- Topic Feature Vectors Refinement --- p.49
Chapter 5 --- Intelligent Web Exploration --- p.51
Chapter 5.1 --- Introduction to Simulated Annealing --- p.51
Chapter 5.2 --- Intelligent Web Exploration by Simulated Annealing --- p.54
Chapter 5.2.1 --- Mathematical Setting of the Discovery Process --- p.57
Chapter 5.2.2 --- The Entire Exploration Algorithm --- p.58
Chapter 5.3 --- Incorporating with the Relevance Feedback Model --- p.60
Chapter 6 --- Experimental Results --- p.61
Chapter 6.1 --- The Design of the Experiments --- p.61
Chapter 6.2 --- Experiments on the Effects of the Simulated Annealing Schedule upon the Discovery Precision --- p.65
Chapter 6.2.1 --- Experiment Setup --- p.66
Chapter 6.2.2 --- Results --- p.66
Chapter 6.3 --- Experiments on the Index Page Topic Profile Specification --- p.72
Chapter 6.3.1 --- Experiment Setup --- p.72
Chapter 6.3.2 --- Results --- p.73
Chapter 6.4 --- Experiments on the Relevance Feedback with Full-Text Feature Extraction Strategy --- p.75
Chapter 6.4.1 --- Experiment Setup --- p.75
Chapter 6.4.2 --- Results --- p.76
Chapter 6.5 --- Comparisons of the Relevance Feedback Feature Extraction Strate- gies --- p.78
Chapter 6.5.1 --- Experiment Setup --- p.78
Chapter 6.5.2 --- Results --- p.79
Chapter 6.6 --- Comparisons between the Example Page and the Keyword Topic Profile Specifications --- p.82
Chapter 6.6.1 --- Experiment Setup --- p.83
Chapter 6.6.2 --- Results --- p.83
Chapter 6.7 --- Summary from the Experimental Results --- p.87
Chapter 7 --- Conclusion --- p.91
Chapter 7.1 --- The Aim of Our Proposed System --- p.91
Chapter 7.2 --- The Favorable Features and the Effectiveness of Our Proposed System --- p.92
Chapter 7.3 --- Future Work --- p.94
Appendix --- p.96
Chapter A --- List of URLs for the Example Pages --- p.96
Chapter B --- List of URLs for the Arbitrarily Chosen Index Pages --- p.98
Bibliography --- p.100
Lo, Yi-Jong, and 羅一中. "Using non-relevance feedback to enhance personalized search." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/3c49e3.
Full text國立臺北科技大學
資訊工程系研究所
98
With advances in Web technology, people can easily search lots of information on the Web by using search engines. Given user queries, general Web search only cares about the relevant documents for most of the users. Thus, personalized search has become an important research topic since it concerns about different user’s preferences. Now there are several ways to implement personalized search: predefining user custom search, classifying user preferences by analyzing their previous behaviors, and using collaborative filtering or recommender systems to find items from similar users to give recommendations. This paper proposes two suggestions. First, we add non-relevant items in search results to avoid inconsistent feedback. The goal is filtering out incorrect user behavior. Second, we propose a dynamic proportional personalized search system that mix personal search and click history, similar user history, and general search result according to their adaptive proportions. From the experimental results, users can find what they want with a higher accuracy by using our search system. The results also show that users get a higher satisfaction score with our system.
Lin, Shen-mu, and 林伸穆. "Applying Novel Relevance Feedback in Query Expansion Enhancement." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/28437136319094505566.
Full text國立雲林科技大學
資訊管理系碩士班
94
Query Expansion was designed to overcome the barren query words issued by the user and has been applied in many commercial products. This treatment tries to expand query words to identify users’ real requirement based on semantic computation. It may be critical to deal with the problem of information overloading and diminish the using threshold, however the modern retrieval systems usually lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. In this study, we propose the LLSF method based on each individual search history to automatically generate specific personalized profile matrix. By which to generate context-based expanded query words. Considering the accuracy of retrieving performance, we process query words re-weighting and document pooling algorithm to achieve this goal. Finally, the documents list is ranked by the way of stressed density distribution modeling. And the experimental results show that our framework corresponds to personalization and the performance is very promising.
Lin, Yi-Lain, and 林宜聯. "A Study of Image Retrieval and Relevance Feedback." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/13129725875136801854.
Full text國立東華大學
資訊工程學系
92
Content-Based Image Retrieval (CBIR) takes visual features, instead of textual annotations, of images as the keys to retrieval images. This thesis presents a novel feature called “frequency layer” that reveals both the color and shape of image contents with respect to different frequencies of textures from the perspective of human vision. As every user may have his own subjective perception on the same image contents, each extracted key of visual feature should be of different importance for different users. This thesis also proposes two methods of relevance feedback to handle this problem of users’ subjectivity. One is designed on a neural network (NN) approach, while the other is based on the technique of maximum likelihood estimation (MLE). In to the experimental results in this research work, the performance of both methods are evaluated and compared. The results on testing some image databases show that both methods are effective. Moreover, the MLE method outperforms the NN approach.
Wu, Ke-neng, and 吳克能. "Applying Relevance Feedback to Improving Text Classification Performance." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/86044801840439396200.
Full text國立中央大學
資訊管理研究所
99
With the rapid development of the Internet, the information explosion across the Internet offers access to an increasing amount of information. Information retrieval system is playing an important role in the information retrieval process. In order to improve the retrieval quality and provide information in line with users’ need, “text classification” is an important issue. The study proposes an approach extracting information of relevance feedback to construct user profile for feature selection and term weighting adjustment of documents, and this approach consists of two concepts: (1) The user profile represents positive and negative interests of user, and the documents preserve only the features belonging to the user profile for reducing the noise interference in text classification. (2) The terms appearing in the user profile or important position in document are weighted for increasing the characteristic difference between relevant and non-relevant documents. Characteristic enhancement of documents is the application of term sensitivity aided by semi-structured information. The results of the experiments show that the proposed approach can extract information of relevance feedback effectively. Not only improving the accuracy of text classification but also at least a half of processing time can be greatly reduced.
Lee, Hung-Yi, and 李宏毅. "Spoken Content Retrieval - Relevance Feedback, Graphs and Semantics." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/93584222510389974705.
Full text國立臺灣大學
電信工程學研究所
100
Multimedia content over the Internet is very attractive, while the spoken part of such content very often tells the core information. Therefore, spoken content retrieval will be very important in helping users retrieve and browse efficiently across the huge qualities of multimedia content in the future. There are usually two stages in typical spoken content retrieval approaches. In the first stage, the audio content is recognized into text symbols by an Automatic Speech Recognition (ASR) system based on a set of acoustic models and language models. In the second stage, after the user enters a query, the retrieval engine searches through the recognition output and returns to the user a list of relevant spoken documents or segments. If the spoken content can be transcribed into text with very high accuracy, the problem is naturally reduced to text information retrieval. However, the inevitable high recognition error rates for spontaneous speech under a wide variety of acoustic conditions and linguistic context make this never possible. In this thesis, the above standard two-stage architecture is completely broken, and the two stages of recognition and retrieval are mixed up and considered as a whole. A set of approaches beyond retrieving over recognition output has been developed here. This idea is very helpful for spoken content retrieval, and may become one of the main future directions in this area. To consider the two stages of recognition and retrieval as a whole, it is proposed to adjust the acoustic model parameters borrowing the techniques of discriminative training but based on user relevance feedback. The problem of retrieval oriented acoustic model re-estimation is different from the conventional acoustic model training approaches for speech recognition in at least two ways: 1. The model training information includes only whether a spoken segment is relevant to a query or not; it does not include the transcription of any utterance. 2. The goal is to improve retrieval performance rather than recognition accuracy. A set of objective functions for retrieval oriented acoustic model re-estimation is proposed to take the properties of retrieval into consideration. There have been some previous works in spoken content retrieval taking advantage of the discriminative capability of machine learning methods. Different from the previous works that derive information from recognition output as features, acoustic vectors such as MFCC are taken as the features for discriminating relevant and irrelevant segments, and they are successfully applied on the scenario of Pseudo Relevance Feedback (PRF). The recognition process can be considered as ``quantization'', in which the acoustic vector sequences are quantized into word symbols. Because different vector sequences may be quantized into the same symbol, much of the information in the spoken content may be lost in the stage of speech recognition. Information directly from the acoustic vector space is considered to compensate for the recognition output in this thesis. This is realized by either PRF or a graph-based re-ranking approach considering the similarity structure among all the segments retrieved. This approach is successfully applied on not only word-based retrieval system but also subword-based system, and these approaches improve the results of Out-of-Vocabulary (OOV) queries as well. The task of Spoken Term Detection (STD) is mainly considered in this thesis, for which the goal is simply returning spoken segments that contain the query terms. Although most works in spoken content retrieval nowadays continue to focus on STD, in this thesis a more general task is also considered: to retrieve the spoken documents semantically related to the queries, no matter the query terms are included in the spoken documents or not. Taking ASR transcriptions as text, the techniques such as latent semantic analysis or query expansion developed for text-based information retrieval can be directly applied for this task. However, the inevitable recognition errors in ASR transcriptions degrade the performance of these techniques. To have more robust semantic retrieval of spoken documents, the expected term frequencies derived from the lattices are enhanced by acoustic similarity with a graph-based approach. The enhanced term frequencies improve the performance of language modelling retrieval approach, document expansion techniques based on latent semantic analysis, and query expansion methods considering both words and latent topic information.
Ming-Han, Hsieh. "Region-Based Image Retrieval by Use of Relevance Feedback." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200518462800.
Full textLi, Jin-Der, and 李進德. "Content-based Image Retrieval with Object-based Relevance Feedback." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/66283677272079652553.
Full text義守大學
資訊工程學系
88
Content-based retrieval is the most convenient way for querying multimedia data. It has become one of the most important research directions on multimedia information retrieval. Many related researches have been proposed in the past years. In this thesis, we propose a new approach for content-based image retrieval and implement a prototype to evaluate the efficiency and effectiveness of the proposed approach. Three kinds of features in images including color, shape and spatial relationship are used to measure the similarity between two images. The proposed approach extracts the different features from images automatically and stores them in a uniform representation. Two main similarity measures consisting of the object-pairs matching algorithm and the similarity matching algorithm are developed to combine with the relevance feedback mechanism for capturing the user’s information need by interactive user interface. The experimental results are evaluated by precision-recall model, and show that the proposed approach is effective.
"Learning on relevance feedback in content-based image retrieval." 2004. http://library.cuhk.edu.hk/record=b5892070.
Full textThesis (M.Phil.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (leaves 89-103).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.iv
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Content-based Image Retrieval --- p.1
Chapter 1.2 --- Relevance Feedback --- p.3
Chapter 1.3 --- Contributions --- p.4
Chapter 1.4 --- Organization of This Work --- p.6
Chapter 2 --- Background --- p.8
Chapter 2.1 --- Relevance Feedback --- p.8
Chapter 2.1.1 --- Heuristic Weighting Methods --- p.9
Chapter 2.1.2 --- Optimization Formulations --- p.10
Chapter 2.1.3 --- Various Machine Learning Techniques --- p.11
Chapter 2.2 --- Support Vector Machines --- p.12
Chapter 2.2.1 --- Setting of the Learning Problem --- p.12
Chapter 2.2.2 --- Optimal Separating Hyperplane --- p.13
Chapter 2.2.3 --- Soft-Margin Support Vector Machine --- p.15
Chapter 2.2.4 --- One-Class Support Vector Machine --- p.16
Chapter 3 --- Relevance Feedback with Biased SVM --- p.18
Chapter 3.1 --- Introduction --- p.18
Chapter 3.2 --- Biased Support Vector Machine --- p.19
Chapter 3.3 --- Relevance Feedback Using Biased SVM --- p.22
Chapter 3.3.1 --- Advantages of BSVM in Relevance Feedback --- p.22
Chapter 3.3.2 --- Relevance Feedback Algorithm by BSVM --- p.23
Chapter 3.4 --- Experiments --- p.24
Chapter 3.4.1 --- Datasets --- p.24
Chapter 3.4.2 --- Image Representation --- p.25
Chapter 3.4.3 --- Experimental Results --- p.26
Chapter 3.5 --- Discussions --- p.29
Chapter 3.6 --- Summary --- p.30
Chapter 4 --- Optimizing Learning with SVM Constraint --- p.31
Chapter 4.1 --- Introduction --- p.31
Chapter 4.2 --- Related Work and Motivation --- p.33
Chapter 4.3 --- Optimizing Learning with SVM Constraint --- p.35
Chapter 4.3.1 --- Problem Formulation and Notations --- p.35
Chapter 4.3.2 --- Learning boundaries with SVM --- p.35
Chapter 4.3.3 --- OPL for the Optimal Distance Function --- p.38
Chapter 4.3.4 --- Overall Similarity Measure with OPL and SVM --- p.40
Chapter 4.4 --- Experiments --- p.41
Chapter 4.4.1 --- Datasets --- p.41
Chapter 4.4.2 --- Image Representation --- p.42
Chapter 4.4.3 --- Performance Evaluation --- p.43
Chapter 4.4.4 --- Complexity and Time Cost Evaluation --- p.45
Chapter 4.5 --- Discussions --- p.47
Chapter 4.6 --- Summary --- p.48
Chapter 5 --- Group-based Relevance Feedback --- p.49
Chapter 5.1 --- Introduction --- p.49
Chapter 5.2 --- SVM Ensembles --- p.50
Chapter 5.3 --- Group-based Relevance Feedback Using SVM Ensembles --- p.51
Chapter 5.3.1 --- (x+l)-class Assumption --- p.51
Chapter 5.3.2 --- Proposed Architecture --- p.52
Chapter 5.3.3 --- Strategy for SVM Combination and Group Ag- gregation --- p.52
Chapter 5.4 --- Experiments --- p.54
Chapter 5.4.1 --- Experimental Implementation --- p.54
Chapter 5.4.2 --- Performance Evaluation --- p.55
Chapter 5.5 --- Discussions --- p.56
Chapter 5.6 --- Summary --- p.57
Chapter 6 --- Log-based Relevance Feedback --- p.58
Chapter 6.1 --- Introduction --- p.58
Chapter 6.2 --- Related Work and Motivation --- p.60
Chapter 6.3 --- Log-based Relevance Feedback Using SLSVM --- p.61
Chapter 6.3.1 --- Problem Statement --- p.61
Chapter 6.3.2 --- Soft Label Support Vector Machine --- p.62
Chapter 6.3.3 --- LRF Algorithm by SLSVM --- p.64
Chapter 6.4 --- Experimental Results --- p.66
Chapter 6.4.1 --- Datasets --- p.66
Chapter 6.4.2 --- Image Representation --- p.66
Chapter 6.4.3 --- Experimental Setup --- p.67
Chapter 6.4.4 --- Performance Comparison --- p.68
Chapter 6.5 --- Discussions --- p.73
Chapter 6.6 --- Summary --- p.75
Chapter 7 --- Application: Web Image Learning --- p.76
Chapter 7.1 --- Introduction --- p.76
Chapter 7.2 --- A Learning Scheme for Searching Semantic Concepts --- p.77
Chapter 7.2.1 --- Searching and Clustering Web Images --- p.78
Chapter 7.2.2 --- Learning Semantic Concepts with Relevance Feed- back --- p.73
Chapter 7.3 --- Experimental Results --- p.79
Chapter 7.3.1 --- Dataset and Features --- p.79
Chapter 7.3.2 --- Performance Evaluation --- p.80
Chapter 7.4 --- Discussions --- p.82
Chapter 7.5 --- Summary --- p.82
Chapter 8 --- Conclusions and Future Work --- p.84
Chapter 8.1 --- Conclusions --- p.84
Chapter 8.2 --- Future Work --- p.85
Chapter A --- List of Publications --- p.87
Bibliography --- p.103
Wu, Jyun-Yue, and 吳君岳. "Adaptive Image Retrieval by Relevance Feedback onPositive/Negative Examples." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/hhns27.
Full text國立東華大學
資訊工程學系
95
It is a difficult but need to proceeding task to understand users’ demand in a content-based image retrieval system. The subjectivity of different users will affect the retrieval results. Relevance feedback(RF) is an effective tool for taking the user’s subjectivity into account. RF provides a scheme that understands the user’s demand and reduces the subjectivity of different users. In this paper, we present a new RF framework: there are two image sets, one is the positive images set and the other one is the negative images set which are both classified by the users. After the classification, the maximum likelihood estimator is used to find the best query points and feature weights in the two image sets individually. Through the learning scheme, the parameters of the two image sets are adjusted by the retrieval results to approach the user’s demand and improve the retrieval efficiency. The experimental results show that the relevance feedback method proposed in this paper compares with some existing methods.
Hsieh, Ming-Han, and 謝明翰. "Region-Based Image Retrieval by Use of Relevance Feedback." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/83936610906625897051.
Full text國立臺灣大學
資訊工程學研究所
93
With the exponential growth of multi-media data, finding images in a large database has become more difficult. Region-based image retrieval (RBIR) is used for solving this problem in this thesis. There are some differences between RBIR and traditional content-based image retrieval (CBIR) systems. CBIR is focused on the similarity of global images and RBIR is focused on the similarity of the local image regions. We apply the watershed segmentation to segment each image into some regions. To classify these regions, the fuzzy k-means clustering algorithm is time-wasting and uses too much space to store the information about the regions. We propose a modified fuzzy k-means clustering algorithm to classify regions efficiently. In order to accelerate our system, we propose a new method for filtering which can filter out many unsuitable images. The candidate images are ranked based on their similarity measure. After our system retrieves the images, the user is able to give feedback to the system. Based on user’s feedback information, our system will retrieve the images that are even closer to the user’s intent.
Lan, Ing-Ren, and 藍英仁. "Relevance Feedback for Image Retrieval Using Multi-color Descriptor." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/91847104280984370288.
Full text義守大學
資訊工程學系
92
In the recent years, digital multimedia has been applied for various applications. Effective management becomes an important role for retrieval from database. This thesis aims at building a content-based retrieving system to retrieve desired images from database. There are two main contributions. First, we propose an image feature database based on human’s perception. This method collects the information of center region in an image, The system is divided into a global feature database, regional feature database to the choice of the feature database while searching. The system automatically chooses the suitable feature database in the method to adopt adaptability. Second, we develop a new feedback technique to identify the user’s desire. In this technique, the weights of features are updated based on user’s recognition. Experiment results indicate that the proposed method is effective for content-based image retrieving.
Yan, Rung-Da, and 顏榮達. "Interactive MPEG-4 Video Retrieval System with Relevance Feedback." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/87345185491039142089.
Full textYang, Duen-Chi, and 楊敦淇. "Using Relevance Feedback in Bayesian Probabilistic Mixture Retrieval Model." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/19440194867358068355.
Full text國立成功大學
資訊工程學系碩博士班
91
A procedure is studied for the purpose of retrieval efficiency improvement for text data to save the cost for who eager for information. In this thesis, a new relevance feedback technique is developed and applied for information retrieval. We focus on automatically adapting model parameters to improve the retrieval performance. Traditionally, the “query expansion” and “query term reweighting” are viewed as two popular of relevance feedback approaches. In this study, the retrieval framework is based on the mixture N-gram model. To improve the retrieval performance, we apply the techniques of query expansion as well as query term rewighting for relevance feedback. Furthermore, the top N retrieved documents at the previous iteration are used for adapting query relevent to a new query document model which incorporates more information and is more useful in the retrieval process. Both query term reweighting and document language model adaptation apply ML (Maximum Likelihood) estimation and Expectation-Maximization (EM) algorithm to estimation the best parameters. In the experiments, the Xinhua news of TDT2 corpus are adopted. We find that the experimental results using query term reweighting and document model adaptation are desirable. If we combine three relevance feedback approaches, the results are further improved compared to using individual approach. Map language model adaptation achieves better performance than ML adaptation in the information retrieval system.
Tao, Ju-Lan, and 陶如蘭. "Content-Based Image Retrieval by Use of Relevance Feedback." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/31603013491059749097.
Full text國立臺灣大學
資訊工程學研究所
90
The aim of this thesis is to build a content-based image retrieval system. It is to retrieve the desired images for a user from a large image database, based on the image contents. In the thesis, a system is implemented and two contributions are made for content-based image retrieval. One is the design of a new feature based on the results of multi-scale watershed image segmentation, which measures the distribution of colors and sizes of segmented regions. The other one is the derivation of a new method for relevance feedback based on Bayesian formulation. In this method, the problem of content-based image retrieval is first formulated as a two-class classification problem, where each image in the database is classified as either “relevant” or “non-relevant” with respect to the query and the goal is to minimize the misclassification error. Next, the problem of image retrieval is transferred into a simpler problem of ranking each image in the database by using a similarity measure that is basically a likelihood ratio. The experimental results have indicated that the proposed method has potential to become practical for content-based image retrieval.
Liu, Yi-Min, and 柳依旻. "Adaptive Relevance Feedback Techniques for Content-Based Image Retrieval." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/18126839365646713051.
Full text國立暨南國際大學
資訊管理學系
95
Due to the popularity of Internet and the growing demand of image access, the volume of image databases is exploding. Hence, we need a more efficient and effective image searching technology. Relevance feedback (RF) is an interaction process between the user and the system such that the user’s information need is satisfied by the retrievals from the system. Traditional RF techniques use the same system parameter values for all types of query images. It is questionable that the best performance can be obtained through such setting. Hence, we propose self-adapting parameterization for the traditional relevance feedback approaches including the query vector modification (QVM) and the feature relevance estimation (FRE) methods using the particle swarm optimization. As such different system parameter values can be used to handle various types of queries, the retrieval system is thus more efficient and effective.
Chan, Li-Wei. "Content-Based Object Movie Retrieval by Use of Relevance Feedback." 2004. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200419433500.
Full text