Content Based Image Retrieval
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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.
Content Based Image And Video Retrieval
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Author : Oge Marques
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-04-30
Content Based Image And Video Retrieval written by Oge Marques 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 2002-04-30 with Computers categories.
Content-Based Image And Video Retrieval addresses the basic concepts and techniques for designing content-based image and video retrieval systems. It also discusses a variety of design choices for the key components of these systems. This book gives a comprehensive survey of the content-based image retrieval systems, including several content-based video retrieval systems. The survey includes both research and commercial content-based retrieval systems. Content-Based Image And Video Retrieval includes pointers to two hundred representative bibliographic references on this field, ranging from survey papers to descriptions of recent work in the area, entire books and more than seventy websites. Finally, the book presents a detailed case study of designing MUSE–a content-based image retrieval system developed at Florida Atlantic University in Boca Raton, Florida.
Multimedia Information Retrieval And Management
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Author : David Feng
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Multimedia Information Retrieval And Management written by David Feng 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 2013-04-17 with Technology & Engineering categories.
Multimedia information technologies, which provide comprehensive and intuitive information for a broad range of applications, have a strong impact on modem life, and have changed our way of learning and thinking. Over the past two decades, there has been an explosive growth in the use of digital multimedia (including audio, video, images and graphics) over the Internet and wireless communication. As the use of digital multimedia increases, effective data storage and management become increasingly important. In fields which use large quantities of data (e. g. audio, video, image and digital libraries; geographical and medical image databases; etc), we need to minimize the volume of data stored while meeting the often conflicting demand for accurate data representation. In addition, the data need to be managed such that it facilitates efficient searching, browsing and cooperative work. This area has been a very active research area in recent years. This book will provide readers with an up-to-date and comprehensive picture of cutting edge technologies in multimedia information retrieval and management, which directly affect our industry, economy and social life The book is divided into two major parts: Technological Fundamentals which covers the core theories of the area; and Applications which describes the broad range of practical uses for this technology.
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.
Content Based Retrieval Of Medical Images
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Author : Paulo Mazzoncini de Azevedo Marques
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2013
Content Based Retrieval Of Medical Images written by Paulo Mazzoncini de Azevedo Marques and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computers categories.
"The aim of this book is to present some of the recent developments in the areas of CBIR [content-based image retrieval] and CAD [computer-aided diagnosis], with particular reference to mammography and breast cancer"--Preface.
Multimedia Systems And Content Based Image Retrieval
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Author : Sagarmay Deb
language : en
Publisher: Idea Group Pub
Release Date : 2004
Multimedia Systems And Content Based Image Retrieval written by Sagarmay Deb and has been published by Idea Group Pub this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Technology & Engineering categories.
Multimedia systems and content-based image retrieval are very important areas of research in computer technology. These two areas are changing our life-styles because together they cover creation, maintenance, accessing and retrieval of video, audio, image, textual and graphic data. Multimedia Systems and Content-Based Image Retrieval addresses unresolved issues and highlights current research.
Artificial Intelligence For Maximizing Content Based Image Retrieval
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Author : Ma, Zongmin
language : en
Publisher: IGI Global
Release Date : 2009-01-31
Artificial Intelligence For Maximizing Content Based Image Retrieval written by Ma, Zongmin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-01-31 with Computers categories.
Discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field.
Challenges And Applications For Implementing Machine Learning In Computer Vision
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Author : Kashyap, Ramgopal
language : en
Publisher: IGI Global
Release Date : 2019-10-04
Challenges And Applications For Implementing Machine Learning In Computer Vision written by Kashyap, Ramgopal and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-04 with Computers categories.
Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.