Relevant bibliographies by topics / Visual thesaurus

Academic literature on the topic 'Visual thesaurus'

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Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Visual thesaurus"

Worrell, Sharon. "Visual thesaurus." ACM SIGCAS Computers and Society 38, no. 3 (September 2008): 20–22. http://dx.doi.org/10.1145/1413872.1413876.

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Rorvig, M. E., C. H. Turner, and J. Moncada. "The NASA Image Collection Visual Thesaurus." Journal of the American Society for Information Science 50, no. 9 (1999): 794–98. http://dx.doi.org/10.1002/(sici)1097-4571(1999)50:9<794::aid-asi8>3.0.co;2-y.

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Avery, Peter, Alexandra D’Arcy, and Keren Rice. "Introduction." Canadian Journal of Linguistics/Revue canadienne de linguistique 51, no. 2-3 (November 2006): 99–104. http://dx.doi.org/10.1017/s0008413100003996.

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The past twenty-five years have seen an enormous growth in research into World Englishes, reflecting a new recognition of the status of the many national English dialects. Canadian English has been no exception to this trend and we have witnessed a dramatic increase in the quantity and quality of research into this variety. The stature it is now accorded as a separate dialect of English is reflected in the number of dictionaries, thesauruses, usage guides, and other reference works published since the late 1990s that were devoted specifically to Canadian English (e.g., Guide to Canadian English usage; Canadian Oxford dictionary; Oxford Canadian Dictionary; Collins essential Canadian English dictionary and thesaurus; Fitzhenry and Whiteside Canadian thesaurus) as well as audio-visual productions (e.g., Dialects of Canadian English; Talking Canadian).

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Mandal, S. "Controlled Vocabulary System for Knowledge Organization: designing a framework." World Digital Libraries: An International Journal 14, no. 2 (2021): 111–24. http://dx.doi.org/10.18329/09757597/2021/14206.

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The paper, discussed in this article, aims to construct a controlled vocabulary and thesaurus construction in libraries for the users. All the terms have been displayed in graphical visual vocabulary regarding the different types of terms and associated terms. It explores how to add different terms and subordinated terms by using the TemaTres open source tool and techniques, process, and steps for designing and developing the controlled vocabulary. This is practical work in thesaurus construction or in controlled vocabulary. Designing thesaurus construction is made using TemaTres and Visual Vocabulary. It provides easy access to library resources relating to different facets. Apart from these it can also display the adjacent terms which can increase the recall and accuracy value of the Web 2.0 environment in the information retrieval system. The paper provides the designing an integrated controlled vocabulary framework for libraries. This integrated framework is useful for constructing the thesaurus. The controlled vocabulary and thesaurus construction is an important component in the information retrieval system. This research paper has shown the process and steps of thesaurus construction and visual vocabulary against different terms for easy access of digital library resources. Finally this concept is more practical to all the public libraries and other educational libraries for easy access of vocabulary terms in both offline and online environments.

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Shiri, Ali, Stan Ruecker, Karl Anvik, and Ximena Rossello. "Thesaurus-enhanced visual interfaces for multilingual information retrieval." Proceedings of the American Society for Information Science and Technology 43, no. 1 (October 10, 2007): 1–7. http://dx.doi.org/10.1002/meet.14504301235.

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Lim, Joo Hwee. "Photograph retrieval and classification by visual keywords and thesaurus." New Generation Computing 18, no. 2 (June 2000): 147–56. http://dx.doi.org/10.1007/bf03037593.

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Spyrou, Evaggelos, Giorgos Tolias, Phivos Mylonas, and Yannis Avrithis. "Concept detection and keyframe extraction using a visual thesaurus." Multimedia Tools and Applications 41, no. 3 (November 8, 2008): 337–73. http://dx.doi.org/10.1007/s11042-008-0237-9.

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O’dell, Allison Jai. "The Visual Vocabulary: Skos:example and the IllustratedArtists’ Books Thesaurus." Journal of Library Metadata 15, no. 3-4 (October 2, 2015): 241–51. http://dx.doi.org/10.1080/19386389.2015.1103086.

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Shi, Chao, and Ling Rothrock. "An Eye-Tracking Evaluation of Gauge Shapes in Process Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (November 2019): 2201–5. http://dx.doi.org/10.1177/1071181319631235.

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Operators monitor gauge shapes in control room displays to maintain the status of the refinery control process within normal limits. To test if shapes currently used by operators are effectively designed, two experiments were conducted to test the effectiveness of individual shapes in a Visual Thesaurus toolkit for comprehension and change detection. Moreover, eye tracking technology was used to investigate basic perceptual recognition behaviors of each Visual Thesaurus shape for comprehension and change detection. Thirty-one students from Pennsylvania State University participated in this experiment. It was found that pupil size could be used to track the cognitive load of participants. Correctness was also measured but it was not as sensitive as pupil size, as it did not capture a difference in the effectiveness of shapes.

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Pham, Nhut Minh, Hieu Quang Pham, Thi Hieu Luong, and Quan Hai Vu. "Hybrid operations for content-based Vietnamese agricultural multimedia information retrieval." Science and Technology Development Journal 18, no. 4 (December 30, 2015): 51–63. http://dx.doi.org/10.32508/stdj.v18i4.909.

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Content-based multimedia information retrieval is never a trivial task even with state-of-the-art approaches. Its mandatory challenge, called “semantic gap,” requires much more understanding of the way human perceive things (i.e., visual and auditory information). Computer scientists have spent thousands of hours seeking optimal solutions, only ended up falling in the bound of this gap for both visual and spoken contexts. While an over-the-gap approach is unreachable, we insist on assembling current viable techniques from both contexts, aligned with a domain concept base (i.e., an ontology), to construct an info service for the retrieval of agricultural multimedia information. The development process spans over three packages: (1) building a Vietnamese agricultural thesaurus; (2) crafting a visual-auditory intertwined search engine; and (3) system deployment as an info service. We spring our the thesaurus in 2 sub-boughs: the aquaculture ontology consists of 3455 concepts and 5396 terms, with 28 relationships, covering about 2200 fish species and their related terms; and the plant production ontology comprises of 3437 concepts and 6874 terms, with 5 relationships, covering farming, plant production, pests, etc. These ontologies serve as a global linkage between keywords, visual, and spoken features, as well as providing the reinforcement for the system performances (e.g., through query expansion, knowledge indexing…). On the other hand, constructing a visual-auditory intertwined search engine is a bit trickier. Automatic transcriptions of audio channels are marked as the anchor points for the collection of visual features. These features, in turn, got clustered based on the referenced thesauri, and ultimately tracking out missing info induced by the speech recognizer’s word error rates. This compensation technique bought us back 14 % of loss recall and an increase of 9 % accuracy over the baseline system. Finally, wrapping the retrieval system as an info service guarantees its practical deployment, asour target audiences are the majority of farmers in developing countries who are unable to reach modern farming information and knowledge.

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Dissertations / Theses on the topic "Visual thesaurus"

Ramsey, Marshall C., Hsinchun Chen, and Bin Zhu. "A Collection of Visual Thesauri for Browsing Large Collections of Geographic Images." John Wiley & Sons, Inc, 1999. http://hdl.handle.net/10150/106407.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
Digital libraries of geo-spatial multimedia content are currently deficient in providing fuzzy, concept-based retrieval mechanisms to users. The main challenge is that indexing and thesaurus creation are extremely laborintensive processes for text documents and especially for images. Recently, 800,000 declassified satellite photographs were made available by the United States Geological Survey. Additionally, millions of satellite and aerial photographs are archived in national and local map libraries. Such enormous collections make human indexing and thesaurus generation methods impossible to utilize. In this article we propose a scalable method to automatically generate visual thesauri of large collections of geo-spatial media using fuzzy, unsupervised machine-learning techniques.

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"Visual thesaurus for color image retrieval using SOM." 2003. http://library.cuhk.edu.hk/record=b5891759.

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Yip King-Fung.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (leaves 84-89).
Abstracts in English and Chinese.
Abstract --- p.i
論文摘要 --- p.iii
Table of Contents --- p.iv
List of Abbreviations --- p.vi
Acknowledgements --- p.vii
Chapter 1. --- Introduction --- p.1
Chapter 1.1. --- Background --- p.1
Chapter 1.2. --- Motivation --- p.3
Chapter 1.3. --- Thesis Organization --- p.4
Chapter 2. --- A Survey of Content-based Image Retrieval --- p.5
Chapter 2.1. --- Text-based Image Retrieval --- p.5
Chapter 2.2. --- Content-Based Image Retrieval --- p.7
Chapter 2.2.1. --- Content-Based Image Retrieval Systems --- p.7
Chapter 2.2.2. --- Query Methods --- p.9
Chapter 2.2.3. --- Image Features --- p.11
Chapter 2.2.4. --- Summary --- p.16
Chapter 3. --- Visual Thesaurus using SOM --- p.17
Chapter 3.1. --- Algorithm --- p.17
Chapter 3.1.1. --- Image Representation --- p.17
Chapter 3.1.2. --- Self-Organizing Map --- p.21
Chapter 3.2. --- Preliminary Experiment --- p.27
Chapter 3.2.1. --- Feature differences --- p.27
Chapter 3.2.2. --- Labeling differences --- p.30
Chapter 4. --- Experiment --- p.33
Chapter 4.1. --- Subjects --- p.33
Chapter 4.2. --- Apparatus --- p.33
Chapter 4.2.1. --- Systems --- p.33
Chapter 4.2.2. --- Test Databases --- p.33
Chapter 4.3. --- Procedure --- p.34
Chapter 4.3.1. --- Description --- p.35
Chapter 4.3.2. --- SOM (text) --- p.36
Chapter 4.3.3. --- SOM (image) --- p.38
Chapter 4.3.4. --- QBE (text) --- p.40
Chapter 4.3.5. --- QBE (image) --- p.42
Chapter 4.3.6. --- Questionnaire --- p.44
Chapter 4.3.7. --- Experiment Flow --- p.45
Chapter 4.4. --- Results --- p.46
Chapter 4.5. --- Discussion --- p.51
Chapter 5. --- Quantizing Color Histogram --- p.55
Chapter 5.1. --- Algorithm --- p.56
Chapter 5.1.1. --- Codebook Generation Phrase --- p.57
Chapter 5.1.2. --- Histogram Generation Phrase --- p.66
Chapter 5.2. --- Experiment --- p.67
Chapter 5.2.1. --- Test Database --- p.67
Chapter 5.2.2. --- Evaluation Methods --- p.67
Chapter 5.2.3. --- Results and Discussion --- p.69
Chapter 5.2.4. --- Summary --- p.74
Chapter 6. --- Relevance Feedback --- p.75
Chapter 6.1. --- Relevance Feedback in Text Information Retrieval --- p.75
Chapter 6.2. --- Relevance Feedback in Multimedia Information Retrieval --- p.76
Chapter 6.3. --- Relevance Feedback in Visual Thesaurus --- p.76
Chapter 7. --- Conclusions --- p.80
Chapter 7.1. --- Applications --- p.81
Chapter 7.2. --- Future Directions --- p.81
Chapter 7.2.1. --- SOM Generation --- p.81
Chapter 7.2.2. --- Hybrid Architecture --- p.82
References --- p.84

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Chen, Yanzhi. "Efficient and robust image ranking for object retrieval." Thesis, 2013. http://hdl.handle.net/2440/90334.

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This thesis focuses on efficient and effective object retrieval from an unlabelled collection of images. The goal of object retrieval is to, given a query image depicting an object, return the dataset images containing that same object, quickly and accurately. Due to its simplicity and efficiency, it is common to use a “Bag-of-Words” (BoW) model in which each image is represented as a weighted vector of quantised features, known as visual words. Although the BoW retrieval system is efficient, the extraction and quantisation of local image features introduces errors into the retrieval results. We build our retrieval system on the BoW model, proposing three kinds of method to improve the retrieval accuracy: i) refinement of BoW image representation; ii) refinement of image similarity; iii) retrieval result re-ranking. Firstly, a visual thesaurus structure is proposed to discover the spatial relatedness of visual words. Based on these, a spatial expansion method is able to enrich the original query with those spatially related visual words (enriched by a general thesaurus) and spatially related foreground words (enriched by an object-based thesaurus). Therefore, the BoW image representation is improved. The second contribution improves the standard image similarity used in the BoW retrieval system such that the similarity between query/dataset images is better described. We do this by a cross-word image matching scheme, such that matching features mapped to different visual words are able to contribute to the similarity score. Thirdly, we also aim at efficient result re-ranking methods to improve the initial retrieval results. We present two re-ranking methods in this thesis. A context based re-ranking method is based on the analysis of correlated subsets of image dataset, called “contexts”. Images that share contexts are weakly correlated to each other, and should therefore mutually influence each other’s ranking. The initial ranking scores are refined by this contextual information. We also present a ranking verification method that is able to extract a set of reliable query relevant images from the retrieved results and thus can be applied in a number of object retrieval applications. Note that neither method needs to recover low level feature information or prior knowledge from the dataset. Instead, they utilize ranking information during run time. We also revisit the definition of the object retrieval problem and propose a group-query method, in which the query is a collection of images depicting the same object instead of a single query image used in the traditional “query-by-example” methods.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2013

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Books on the topic "Visual thesaurus"

Blob Visual Emotional Thesaurus. Taylor & Francis Group, 2017.

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Long, Ian. Blob Visual Emotional Thesaurus. Taylor & Francis Group, 2017.

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Long, Ian. Blob Visual Emotional Thesaurus. Taylor & Francis Group, 2017.

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Long, Ian. Blob Visual Emotional Thesaurus. Taylor & Francis Group, 2017.

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Long, Ian. Blob Visual Emotional Thesaurus. Taylor & Francis Group, 2017.

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United States. National Aeronautics and Space Administration., ed. NASA visual thesaurus maintenance documentation. [Houston, Tex.?]: Research Institute for Computing and Information Systems, University of Houston-Clear Lake, 1989.

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Long, Ian. The Blob Visual Emotional Thesaurus. Routledge, 2017. http://dx.doi.org/10.4324/9781315174181.

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Odell, Lee, and Susan M. Katz. Writing in a Visual Age & paperback thesaurus. Bedford/St. Martin's, 2005.

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Goveia, Chris, and Hatmaker. Visual Thesaurus: A Quick Flip Brainstorming Tool for Graphic Designers. Rockport Publishers, 2003.

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Willis, Alfred. Festschrift for the Art and Architecture Thesaurus: A special issue of the journal Visual Resources. Taylor and Francis, 1995.

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Book chapters on the topic "Visual thesaurus"

Malaisé, Véronique, Lora Aroyo, Hennie Brugman, Luit Gazendam, Annemieke de Jong, Christian Negru, and Guus Schreiber. "Evaluating a Thesaurus Browser for an Audio-visual Archive." In Managing Knowledge in a World of Networks, 272–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11891451_25.

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Gupta, Parth, Alberto Barrón-Cedeño, and Paolo Rosso. "Cross-Language High Similarity Search Using a Conceptual Thesaurus." In Information Access Evaluation. Multilinguality, Multimodality, and Visual Analytics, 67–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33247-0_8.

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Shiri, Ali, Stan Ruecker, Lindsay Doll, Matthew Bouchard, and Carlos Fiorentino. "An Evaluation of Thesaurus-Enhanced Visual Interfaces for Multilingual Digital Libraries." In Research and Advanced Technology for Digital Libraries, 236–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24469-8_25.

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Shaila, S. G., and A. Vadivel. "Constructing Thesaurus Using TAG Term Weight for Query Expansion in Information Retrieval Application." In Textual and Visual Information Retrieval using Query Refinement and Pattern Analysis, 55–75. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2559-5_3.

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"relieved." In The Blob Visual Emotional Thesaurus, 256–340. Routledge, 2017. http://dx.doi.org/10.4324/9781315174181-11.

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"A range of scared feelings." In The Blob Visual Emotional Thesaurus, 13. Routledge, 2017. http://dx.doi.org/10.4324/9781315174181-9.

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"A range of angry feelings." In The Blob Visual Emotional Thesaurus, 9. Routledge, 2017. http://dx.doi.org/10.4324/9781315174181-5.

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"A range of sad feelings." In The Blob Visual Emotional Thesaurus, 8. Routledge, 2017. http://dx.doi.org/10.4324/9781315174181-4.

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"A range of positive feelings." In The Blob Visual Emotional Thesaurus, 11. Routledge, 2017. http://dx.doi.org/10.4324/9781315174181-7.

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"Dispirited to obsessed feelings." In The Blob Visual Emotional Thesaurus, 10. Routledge, 2017. http://dx.doi.org/10.4324/9781315174181-6.

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Conference papers on the topic "Visual thesaurus"

Picard, Rosalind W. "Toward a Visual Thesaurus." In Proceedings of the Final Workshop on Multimedia Information Retrieval (Miro '95). BCS Learning & Development, 1995. http://dx.doi.org/10.14236/ewic/miro1995.13.

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Chen, Yanzhi, Anthony Dick, and Anton van den Hengel. "Image Retrieval with a Visual Thesaurus." In 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2010. http://dx.doi.org/10.1109/dicta.2010.11.

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Fowler, Richard H., Wendy A. L. Fowler, and Bradley A. Wilson. "Integrating query thesaurus, and documents through a common visual representation." In the 14th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1991. http://dx.doi.org/10.1145/122860.122874.

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Frigui, Hichem, and Joshua Caudill. "Mining Visual and Textual Data for Constructing a Multi-Modal Thesaurus." In Proceedings of the 2007 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2007. http://dx.doi.org/10.1137/1.9781611972771.48.

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Williams, Robert, and Heinz Dreher. "Formative Assessment Visual Feedback in Computer Graded Essays." In InSITE 2005: Informing Science + IT Education Conference. Informing Science Institute, 2005. http://dx.doi.org/10.28945/2843.

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In this paper we discuss a simple but comprehensive form of feedback to essay authors, based on a thesaurus and computer graphics, which enables the essay authors to see where essay content is inadequate in terms of the discussion of the essay topic. Concepts which are inadequately covered are displayed for the information of the author so that the essay can be improved. The feedback is automatically produced by the MarkIT Automated Essay Grading system, being developed by Curtin University researchers.

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Houissa, Hichem, and Nozha Boujemaa. "A New Angle-Based Spatial Modeling for Query by Visual Thesaurus Composition." In 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4380077.

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Spyrou, Evaggelos, and Yannis Avrithis. "Keyframe Extraction Using Local Visual Semantics in the Form of a Region Thesaurus." In Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007). IEEE, 2007. http://dx.doi.org/10.1109/smap.2007.39.

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Bahroun, Sahbi, Nozha Boujemaa, and Ziad Belhadj. "Hierarchical visual thesaurus building for satellite image retrieval based on semantic region labelling." In 2010 17th IEEE International Conference on Image Processing (ICIP 2010). IEEE, 2010. http://dx.doi.org/10.1109/icip.2010.5654151.

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Shahhosseini, A. Mehran, Haisong Ye, George Maughan, and Tad Foster. "Implementation of Similarity Flooding Algorithm to Solve Engineering Problems Using Diagnostic Skills Training Technique." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-39698.

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This article focuses on the development and field testing of instructional design concepts and educational software to teach diagnostic skills necessary to identify and solve problems in complex technical systems. Using concept mapping software along with expert-system programs, the overall software package enables technical workers and students in technology and engineering fields to benefit from personalized, iterative interactions that permit them to design visual maps of a diagnostic strategy and to allow direct and automatic comparison of their visual map to an expert’s map. The computer-based modules are developed in the Lectora authoring system and incorporate the VUE concept mapping software. The self-paced, interactive modules include an introduction stage, a visual mapping tutorial stage and a technical system and problem stage. Two technical systems will provide the context for a technical problem, the systems are: 1.) electrical power grid, and 2.) heat exchanger used in a waste plastic pelletizer machine. The educational software can be tailored to include other technical systems and technical problems. This article demonstrates implementation of Similarity Flooding Algorithm (SFA) to solve engineering problems. SFA is used to match nodes and links between learner’s and expert’s concept maps. To compare two process maps, SFA needs to consider both the relations (links) between nodes and the content of the nodes. In this article, we describe four improvements to the original SFA code to improve the comparison between two different concept (or process) maps: 1) Similarity of two strings is calculated by comparing the two strings character by character, which means that two strings get lower similarity if they are not the same exactly. During the comparison, WordNet® thesaurus is used to accurately evaluate the content of nodes; 2) Base similarity includes absolute similarity for each paired nodes, according to their links and content, but the overall similarity of the maps is calculated based on the relative similarity of each pair; 3) In a process map, the importance of each node could be different and important nodes have more weighting in overall similarity; 4) We consider two threshold values in the comparison algorithm. One is similarity threshold for the similarity based on the connections (links) of the nodes; the other is synonym threshold for the semantic similarity of content. If the similarity is lower than the thresholds, it could be considered as discarded. At the end, we analyze the collected data and show the effectiveness of the proposed technique to solve educational and training problems.

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