Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Content-Based Image Retrieval PDF full book. Access full book title Content-Based Image Retrieval by Vipin Tyagi. Download full books in PDF and EPUB format.
Author: Vipin Tyagi Publisher: Springer ISBN: 9811067597 Category : Computers Languages : en Pages : 399
Book Description
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.
Author: Vipin Tyagi Publisher: Springer ISBN: 9811067597 Category : Computers Languages : en Pages : 399
Book Description
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.
Author: Oge Marques Publisher: Springer Science & Business Media ISBN: 1461509874 Category : Computers Languages : en Pages : 189
Book Description
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.
Author: Ma, Zongmin Publisher: IGI Global ISBN: 1605661759 Category : Computers Languages : en Pages : 450
Book Description
Discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field.
Author: David Feng Publisher: Springer Science & Business Media ISBN: 3662053004 Category : Technology & Engineering Languages : en Pages : 494
Book Description
Everything you ever wanted to know about multimedia retrieval and management. This comprehensive book offers a full picture of the cutting-edge technologies necessary for a profound introduction to the field. Leading experts also cover a broad range of practical applications.
Author: Rajkumar Kannan Publisher: Springer ISBN: 3642278728 Category : Computers Languages : en Pages : 352
Book Description
This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Data Engineering and Management, ICDEM 2010, held in Tiruchirappalli, India, in July 2010. The 46 revised full papers presented together with 1 keynote paper and 2 tutorial papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on Digital Library; Knowledge and Mulsemedia; Data Management and Knowledge Extraction; Natural Language Processing; Workshop on Data Mining with Graphs and Matrices.
Author: Suresh Chandra Satapathy Publisher: Springer Science & Business Media ISBN: 3319029312 Category : Technology & Engineering Languages : en Pages : 553
Book Description
This volume contains the papers presented at the Second International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA-2013) held during 14-16 November 2013 organized by Bhubaneswar Engineering College (BEC), Bhubaneswar, Odisha, India. It contains 63 papers focusing on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc for various engineering applications such as data mining, Fuzzy systems, Machine Intelligence and ANN, Web technologies and Multimedia applications and Intelligent computing and Networking etc.
Author: Anindita Mukherjee Publisher: Mohammed Abdul Sattar ISBN: Category : Computers Languages : en Pages : 0
Book Description
Content based image retrieval (CBIR) has become a popular area of research for both computer vision and multimedia communities. It aims at organizing digital picture archives by analyzing their visual contents. CBIR techniques make use of these visual contents to retrieve in response to any particular query. Note that this differs from traditional retrieval systems based on keywords to search images. Due to widespread variations in the images of standard image databases, achieving high precision and recall for retrieval remains a challenging task. In the recent past, many CBIR algorithms have applied Bag of Visual Words (BoVW) for modeling the visual contents of images. Though BoVW has emerged as a popular image content descriptor, it has some important limitations which can in turn adversely affect the retrieval performance. Image retrieval has many applications in diverse fields including healthcare, biometrics, digital libraries, historical research and many more (da Silva Torres and Falcao, 2006). In the retrieval system, two kinds of approaches are mainly followed, namely, Text-Based Image Retrieval (TBIR) and Content-Based Image Retrieval (CBIR). The former approach requires a lot of hu- man effort, and time and perception. Content based image retrieval is a technique that enables an user to extract similar images based on a query from a database containing large number of images.The basic issue in designing a CBIR system is to select the image features that best represent the image content in a database. As a part of a CBIR system, one has to apply appropriate visual content descriptors to represent these images. A query image should be represented similarly. Then, based on some measures of similarity, a set of images would be retrieved from the avail- able image database. The relevance feedback part, which incorporates inputs from a user, can be an optional block in a CBIR system. The fundamental problem in CBIR is how to transform the visual contents into distinctive features for dissimilar images, and into similar features for images that look alike. BoVW has emerged as a popular model for representing the visual content of an image in the recent past. It tries to bridge the gap between low level visual features and high-level semantic features to some extent.
Author: Anbarasan, Kalaivani Publisher: IGI Global ISBN: 1799830934 Category : Medical Languages : en Pages : 248
Book Description
Recent advancements in the technology of medical imaging, such as CT and MRI scanners, are making it possible to create more detailed 3D and 4D images. These powerful images require vast amounts of digital data to help with the diagnosis of the patient. Artificial intelligence (AI) must play a vital role in supporting with the analysis of this medical imaging data, but it will only be viable as long as healthcare professionals and AI interact to embrace deep thinking platforms such as automation in the identification of diseases in patients. AI Innovation in Medical Imaging Diagnostics is an essential reference source that examines AI applications in medical imaging that can transform hospitals to become more efficient in the management of patient treatment plans through the production of faster imaging and the reduction of radiation dosages through the PET and SPECT imaging modalities. The book also explores how data clusters from these images can be translated into small data packages that can be accessed by healthcare departments to give a real-time insight into patient care and required interventions. Featuring research on topics such as assistive healthcare, cancer detection, and machine learning, this book is ideally designed for healthcare administrators, radiologists, data analysts, computer science professionals, medical imaging specialists, diagnosticians, medical professionals, researchers, and students.
Author: Björn Barz Publisher: Cuvillier Verlag ISBN: 3736963467 Category : Computers Languages : en Pages : 322
Book Description
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.
Author: Monica Bianchini Publisher: Springer Science & Business Media ISBN: 3642366570 Category : Technology & Engineering Languages : en Pages : 547
Book Description
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.
Author: Vipin Tyagi
Author: Vipin Tyagi
Author: Oge Marques
Author: Ma, Zongmin
Author: David Feng
Author: Rajkumar Kannan
Author: Suresh Chandra Satapathy
Author: Anindita Mukherjee
Author: Anbarasan, Kalaivani
Author: Björn Barz
Author: Monica Bianchini