VisualInformation Retrieval Using Java and LIRE / Synthesis Lectures on Information Concepts, Retrieval, and Services (PDF)
(Sprache: Englisch)
Visual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) from large, unstructured repositories.
The goal of...
The goal of...
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Visual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) from large, unstructured repositories.
The goal of VIR is to retrieve matches ranked by their relevance to a given query, which is often expressed as an example image and/or a series of keywords. During its early years (1995-2000), the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. During the past decade, it was widely recognized that the challenges imposed by the lack of coincidence between an image's visual contents and its semantic interpretation, also known as semantic gap, required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on.
In this introductory book, we focus on a subset of VIR problems where the media consists of images, and the indexing and retrieval methods are based on the pixel contents of those images -- an approach known as content-based image retrieval (CBIR). We present an implementation-oriented overview of CBIR concepts, techniques, algorithms, and figures of merit. Most chapters are supported by examples written in Java, using Lucene (an open-source Java-based indexing and search implementation) and LIRE (Lucene Image REtrieval), an open-source Java-based library for CBIR.
Table of Contents: Introduction / Information Retrieval: Selected Concepts and Techniques / Visual Features / Indexing Visual Features / LIRE: An Extensible Java CBIR Library / Concluding Remarks
The goal of VIR is to retrieve matches ranked by their relevance to a given query, which is often expressed as an example image and/or a series of keywords. During its early years (1995-2000), the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. During the past decade, it was widely recognized that the challenges imposed by the lack of coincidence between an image's visual contents and its semantic interpretation, also known as semantic gap, required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on.
In this introductory book, we focus on a subset of VIR problems where the media consists of images, and the indexing and retrieval methods are based on the pixel contents of those images -- an approach known as content-based image retrieval (CBIR). We present an implementation-oriented overview of CBIR concepts, techniques, algorithms, and figures of merit. Most chapters are supported by examples written in Java, using Lucene (an open-source Java-based indexing and search implementation) and LIRE (Lucene Image REtrieval), an open-source Java-based library for CBIR.
Table of Contents: Introduction / Information Retrieval: Selected Concepts and Techniques / Visual Features / Indexing Visual Features / LIRE: An Extensible Java CBIR Library / Concluding Remarks
Autoren-Porträt von Mathias Lux, Oge Marques
Mathias Lux is a Senior Assistant Professor at the Institute for InformationTechnology (ITEC) at Klagenfurt University, where he has been since 2006. He received his M.S. in Mathematics in 2004 and his Ph.D. in Telematics in 2006 from Graz University of Technology. Before joining Klagenfurt University, he worked in industry on web-based applications, as a junior researcher at a research center for knowledge-based applications,and as research and teaching assistant at the Knowledge Management Institute (KMI) of Graz University of Technology. In research,he is working on user intentions in multimedia retrieval and production, visual information retrieval, and serious games.In his scientific career he has (co) authored more than 60 scientific publications, has served in multiple program committees and as reviewer of international conferences, journals, and magazines, and has organized several scientific events. He is also well known for managing the development of the award-winning and popular open source tools Caliph & Emir and LIRE for visual information retrieval.Oge Marques is an Associate Professor in the Department of Computer & Electrical Engineering and Computer Science at Florida Atlantic University (FAU) (Boca Raton, Florida). He received his Ph.D. in Computer Engineering from FAU in 2001. He has more than 20 years of teaching and research experience in the fields of image processing and computer vision, in different countries (U.S., Austria, Brazil, Netherlands, Spain, France, and India), languages (English, Portuguese, Spanish), and capacities.He is the (co) author of more than 50 refereed journal and conference papers and several books in these topics, including the textbook Practical Image and Video Processing Using MATLAB (Wiley, 2011). His research interests are in the area of intelligent processing of visual information, which combines the fields of image processing, computer vision, image retrieval, machine learning, serious games, and human visual
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perception. He is particularly interested in the combination of human computation and machine learning techniques to solve computer vision problems. He is a senior member of both the ACM and IEEE, and a member of the IEEE Computer Society, IEEE Education Society, IEEE Signal Processing Society, and the honor societies of Tau Beta Pi, Sigma Xi, Phi Kappa Phi, and Upsilon Pi Epsilon.
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Bibliographische Angaben
- Autoren: Mathias Lux , Oge Marques
- 2022, 1. Auflage, 96 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3031022823
- ISBN-13: 9783031022821
- Erscheinungsdatum: 31.05.2022
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