Semantic And Interactive Content Based Image Retrieval
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Semantic And Interactive Content Based Image Retrieval
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Author : Björn Barz
language : en
Publisher: Cuvillier Verlag
Release Date : 2020-12-23
Semantic And Interactive Content Based Image Retrieval written by Björn Barz and has been published by Cuvillier Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-23 with Computers categories.
Content-based Image Retrieval (CBIR) ist ein Verfahren zum Auffinden von Bildern in großen Datenbanken wie z. B. dem Internet anhand ihres Inhalts. Ausgehend von einem vom Nutzer bereitgestellten Anfragebild, gibt das System eine sortierte Liste ähnlicher Bilder zurück. Der Großteil moderner CBIR-Systeme vergleicht Bilder ausschließlich anhand ihrer visuellen Ähnlichkeit, d.h. dem Vorhandensein ähnlicher Texturen, Farbkompositionen etc. Jedoch impliziert visuelle Ähnlichkeit nicht zwangsläufig auch semantische Ähnlichkeit. Zum Beispiel können Bilder von Schmetterlingen und Raupen als ähnlich betrachtet werden, weil sich die Raupe irgendwann in einen Schmetterling verwandelt. Optisch haben sie jedoch nicht viel gemeinsam. Die vorliegende Arbeit stellt eine Methode vor, welche solch menschliches Vorwissen über die Semantik der Welt in Deep-Learning-Verfahren integriert. Als Quelle für dieses Wissen dienen Taxonomien, die für eine Vielzahl von Domänen verfügbar sind und hierarchische Beziehungen zwischen Konzepten kodieren (z.B., ein Pudel ist ein Hund ist ein Tier etc.). Diese hierarchiebasierten semantischen Bildmerkmale verbessern die semantische Konsistenz der CBIR-Ergebnisse im Vergleich zu herkömmlichen Repräsentationen und Merkmalen erheblich. Darüber hinaus werden drei verschiedene Mechanismen für interaktives Image Retrieval präsentiert, welche die den Anfragebildern inhärente semantische Ambiguität durch Einbezug von Benutzerfeedback auflösen. Eine der vorgeschlagenen Methoden reduziert das erforderliche Feedback mithilfe von Clustering auf einen einzigen Klick, während eine andere den Nutzer kontinuierlich involviert, indem das System aktiv nach Feedback zu denjenigen Bildern fragt, von denen der größte Erkenntnisgewinn bezüglich des Relevanzmodells erwartet wird. Die dritte Methode ermöglicht dem Benutzer die Auswahl besonders interessanter Bildbereiche zur Fokussierung der Ergebnisse. Diese Techniken liefern bereits nach wenigen Feedbackrunden deutlich relevantere Ergebnisse, was die Gesamtmenge der abgerufenen Bilder reduziert, die der Benutzer überprüfen muss, um relevante Bilder zu finden. Content-based image retrieval (CBIR) aims for finding images in large databases such as the internet based on their content. Given an exemplary query image provided by the user, the retrieval system provides a ranked list of similar images. Most contemporary CBIR systems compare images solely by means of their visual similarity, i.e., the occurrence of similar textures and the composition of colors. However, visual similarity does not necessarily coincide with semantic similarity. For example, images of butterflies and caterpillars can be considered as similar, because the caterpillar turns into a butterfly at some point in time. Visually, however, they do not have much in common. In this work, we propose to integrate such human prior knowledge about the semantics of the world into deep learning techniques. Class hierarchies serve as a source for this knowledge, which are readily available for a plethora of domains and encode is-a relationships (e.g., a poodle is a dog is an animal etc.). Our hierarchy-based semantic embeddings improve the semantic consistency of CBIR results substantially compared to conventional image representations and features. We furthermore present three different mechanisms for interactive image retrieval by incorporating user feedback to resolve the inherent semantic ambiguity present in the query image. One of the proposed methods reduces the required user feedback to a single click using clustering, while another keeps the human in the loop by actively asking for feedback regarding those images which are expected to improve the relevance model the most. The third method allows the user to select particularly interesting regions in images. These techniques yield more relevant results after a few rounds of feedback, which reduces the total amount of retrieved images the user needs to inspect to find relevant ones.
Handbook On Neural Information Processing
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Author : Monica Bianchini
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-12
Handbook On Neural Information Processing written by Monica Bianchini and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-12 with Technology & Engineering categories.
This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: Deep architectures Recurrent, recursive, and graph neural networks Cellular neural networks Bayesian networks Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple classifier systems Self organisation and modal learning Applications to content-based image retrieval, text mining in large document collections, and bioinformatics This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.
Semantic Based Visual Information Retrieval
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Author : Zhang, Yu-Jin
language : en
Publisher: IGI Global
Release Date : 2006-11-30
Semantic Based Visual Information Retrieval written by Zhang, Yu-Jin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-30 with Computers categories.
"This book presents state-of-the-art advancements and developments in the field, and also brings a selection of techniques and algorithms about semantic-based visual information retrieval. It covers many critical issues, such as: multi-level representation and description, scene understanding, semantic modeling, image and video annotation, human-computer interaction, and more"--Provided by publisher.
Multimedia Content And The Semantic Web
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Author : Giorgos Stamou
language : en
Publisher: John Wiley & Sons
Release Date : 2005-10-31
Multimedia Content And The Semantic Web written by Giorgos Stamou and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-10-31 with Technology & Engineering categories.
The emerging idea of the semantic web is based on the maximum automation of the complete knowledge lifecycle processes: knowledge representation, acquisition, adaptation, reasoning, sharing and use. Text-based based browsers involve a costly information-retrieval process: descriptions are inherently subjective and usage is often confined to the specific application domain for which the descriptions were created. Automatic extracted audiovisual features are, in general, more objective, domain-independent and can be native to the audiovisual content. This book seeks to draw together in one concise volume the findings of leading researchers from around the globe. The focus, in particular, is on the MPEG-7 and MPEG-21 standards that seek to consolidate and render effective the infrastructure for the delivery and management of multimedia content. Provides thorough coverage of all relevant topics, including structure identification in audiovisual documents, object-based video indexing, multimedia indexing and retrieval using natural language, speech and image processing methods Contains detailed advice on ontology representation and querying for realizing semantics-driven applications Includes cutting-edge information on multimedia content description in MPEG-7 and MPEG-21 Illustrates all theory with real-world case studies gleaned from state-of-the-art worldwide research. The contributors are pioneers in the fields of multimedia analysis and knowledge technologies This unified, comprehensive up-to-date resource will appeal to integrators, systems suppliers, managers and consultants in the area of knowledge management and information retrieval; particularly those concerned with the automation of the semantic web. The detailed, theory-based practical advice is also essential reading for postgraduates and researchers in these fields.
Content Based Image Retrieval
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Author : Vipin Tyagi
language : en
Publisher: Springer
Release Date : 2018-01-15
Content Based Image Retrieval written by Vipin Tyagi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-15 with Computers categories.
The book describes several techniques used to bridge the semantic gap and reflects on recent advancements in content-based image retrieval (CBIR). It presents insights into and the theoretical foundation of various essential concepts related to image searches, together with examples of natural and texture image types. The book discusses key challenges and research topics in the context of image retrieval, and provides descriptions of various image databases used in research studies. The area of image retrieval, and especially content-based image retrieval (CBIR), is a very exciting one, both for research and for commercial applications. The book explains the low-level features that can be extracted from an image (such as color, texture, shape) and several techniques used to successfully bridge the semantic gap in image retrieval, making it a valuable resource for students and researchers interested in the area of CBIR alike.
Advances In Semantic Media Adaptation And Personalization
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Author : Marios C. Angelides
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-01-04
Advances In Semantic Media Adaptation And Personalization written by Marios C. Angelides and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-04 with Computers categories.
Realizing the growing importance of semantic adaptation and personalization of media, the editors of this book brought together leading researchers and practitioners of the field to discuss the state-of-the-art, and explore emerging exciting developments. This volume comprises extended versions of selected papers presented at the 1st International Workshop on Semantic Media Adaptation and Personalization (SMAP 2006), which took place in Athens in December 2006.
Concept Based Video Retrieval
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Author : Cees G. M. Snoek
language : en
Publisher: Now Publishers Inc
Release Date : 2009
Concept Based Video Retrieval written by Cees G. M. Snoek and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
In this paper, we review 300 references on video retrieval, indicating when text-only solutions are unsatisfactory and showing the promising alternatives which are in majority concept-based. Therefore, central to our discussion is the notion of a semantic concept: an objective linguistic description of an observable entity. Specifically, we present our view on how its automated detection, selection under uncertainty, and interactive usage might solve the major scientific problem for video retrieval: the semantic gap. To bridge the gap, we lay down the anatomy of a concept-based video search engine. We present a component-wise decomposition of such an interdisciplinary multimedia system, covering influences from information retrieval, computer vision, machine learning, and human-computer interaction. For each of the components we review state-of-the-art solutions in the literature, each having different characteristics and merits. Because of these differences, we cannot understand the progress in video retrieval without serious evaluation efforts such as carried out in the NIST TRECVID benchmark. We discuss its data, tasks, results, and the many derived community initiatives in creating annotations and baselines for repeatable experiments. We conclude with our perspective on future challenges and opportunities.
Visual Attributes
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Author : Rogerio Schmidt Feris
language : en
Publisher: Springer
Release Date : 2017-03-21
Visual Attributes written by Rogerio Schmidt Feris and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-21 with Computers categories.
This unique text/reference provides a detailed overview of the latest advances in machine learning and computer vision related to visual attributes, highlighting how this emerging field intersects with other disciplines, such as computational linguistics and human-machine interaction. Topics and features: presents attribute-based methods for zero-shot classification, learning using privileged information, and methods for multi-task attribute learning; describes the concept of relative attributes, and examines the effectiveness of modeling relative attributes in image search applications; reviews state-of-the-art methods for estimation of human attributes, and describes their use in a range of different applications; discusses attempts to build a vocabulary of visual attributes; explores the connections between visual attributes and natural language; provides contributions from an international selection of world-renowned scientists, covering both theoretical aspects and practical applications.
Image And Video Retrieval
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Author : Hari Sundaram
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-06-29
Image And Video Retrieval written by Hari Sundaram and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-29 with Computers categories.
Here are the refereed proceedings of the 5th International Conference on Image and Video Retrieval, CIVR 2006, held in Singapore in July 2006. Presents 18 revised full papers and 30 poster papers, together with extended abstracts of 5 papers of 1 special session and those of 10 demonstration papers. These cover interactive image and video retrieval, semantic image retrieval, visual feature analysis, learning and classification, image and video retrieval metrics, and machine tagging.
Computational Collective Intelligence Semantic Web Social Networks And Multiagent Systems
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Author : Ryszard Kowalczyk
language : en
Publisher: Springer
Release Date : 2009-10-04
Computational Collective Intelligence Semantic Web Social Networks And Multiagent Systems written by Ryszard Kowalczyk and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-10-04 with Computers categories.
Computational collective intelligence (CCI) is most often understood as a subfield of artificial intelligence (AI) dealing with soft computing methods that enable group decisions to be made or knowledge to be processed among autonomous units acting in distributed environments. The needs for CCI techniques and tools have grown signi- cantly recently as many information systems work in distributed environments and use distributed resources. Web-based systems, social networks and multi-agent systems very often need these tools for working out consistent knowledge states, resolving conflicts and making decisions. Therefore, CCI is of great importance for today’s and future distributed systems. Methodological, theoretical and practical aspects of computational collective int- ligence, such as group decision making, collective action coordination, and knowledge integration, are considered as the form of intelligence that emerges from the collabo- tion and competition of many individuals (artificial and/or natural). The application of multiple computational intelligence technologies such as fuzzy systems, evolutionary computation, neural systems, consensus theory, etc. , can support human and other collective intelligence and create new forms of CCI in natural and/or artificial s- tems.