Download or read book Contextual Image Retrieval with Active Relevance Feedback written by Xing Xing and published by . This book was released on 2009 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Effective Graph Based Content Based Image Retrieval Systems for Large Scale and Small Scale Image Databases written by Ran Chang and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation proposes two novel manifold graph-based ranking systems for Content-Based Image Retrieval (CBIR). The two proposed systems exploit the synergism between relevance feedback-based transductive short-term learning and semantic feature-based long-term learning to improve retrieval performance. Proposed systems first apply the active learning mechanism to construct users' relevance feedback log and extract high-level semantic features for each image. These systems then create manifold graphs by incorporating both the low-level visual similarity and the high-level semantic similarity to achieve more meaningful structures for the image space. Finally, asymmetric relevance vectors are created to propagate relevance scores of labeled images to unlabeled images via manifold graphs. The extensive experimental results demonstrate two proposed systems outperform the other state-of-the-art CBIR systems in the context of both correct and erroneous users' feedback.

Download or read book Artificial Intelligence for Maximizing Content Based Image Retrieval written by Ma, Zongmin and published by IGI Global. This book was released on 2009-01-31 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field.

Download or read book Content Based Image Retrieval written by Vipin Tyagi and published by Springer. This book was released on 2018-01-15 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Semantic and Interactive Content based Image Retrieval written by Björn Barz and published by Cuvillier Verlag. This book was released on 2020-12-23 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Content Based Image and Video Retrieval written by Oge Marques and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Towards Intelligible Query Processing in Relevance Feedback Based Image Retrieval Systems written by Belkhatir Mohammed and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We have specified within the scope of this paper a framework combining semantics and relational (spatial) characterizations within a coupled architecture in order to address the semantic gap. This framework is instantiated by an operational model based on a sound logic-based formalism, allowing to define a representation for image documents and a matching function to compare index and query structures. We have specified a query framework coupling keyword-based querying with a relevance feedback module managing transparent and penetrable interactions by considering conceptual characterizations of images. The choice of conceptual graphs as an operational model is the most natural in the sense that it holds several advantages in our application context. It indeed allows the symbolic representation of all components of a multimedia indexing and retrieval architecture: queries, index documents and matching function. Moreover its simple representation is particularly well-suited for user interaction in the framework of relevance feedback. To stress the relevance of our approach, the theoretical contributions of this paper in the domain of image indexing and retrieval are summarized below: We have first proposed a neural-network based architecture for the highlighting of image objects, structures abstracting the image visual entites, and the characterization of their associated semantics. In the perspective of unifying the semantic and relational characterizations, we have proposed an integrated model featuring a bi-facetted organization. The visual semantics facet describes the image semantic content and is based on labeling IOs with a semantic concept. The relational facet is itself based on the relational (spatial) characterizations between pairs of image objects obtained after highlighting a correspondence process between extracted low-level information and symbolic relations.

Download or read book Development of a Relevance feedback Image Retrieval System Based on Multiple instance Learning written by Salini Rao Pamidimukkala and published by . This book was released on 2007 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Content based Image Retrieval Using Relevance Feedback written by Oge Marques and published by . This book was released on 2001 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Learning on Relevance Feedback in Content based Image Retrieval written by Chu-Hong Hoi and published by . This book was released on 2004 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Big Data in Medical Image Processing written by R. Suganya and published by CRC Press. This book was released on 2018-01-29 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of medical imaging seen rapid development over the last two decades and has consequently revolutionized the way in which modern medicine is practiced. Diseases and their symptoms are constantly changing therefore continuous updating is necessary for the data to be relevant. Diseases fall into different categories, even a small difference in symptoms may result in categorising it in a different group altogether. Thus analysing data accurately is of critical importance. This book concentrates on diagnosing diseases like cancer or tumor from different modalities of images. This book is divided into the following domains: Importance of big data in medical imaging, pre-processing, image registration, feature extraction, classification and retrieval. It is further supplemented by the medical analyst for a continuous treatment process. The book provides an automated system that could retrieve images based on user’s interest to a point of providing decision support. It will help medical analysts to take informed decisions before planning treatment and surgery. It will also be useful to researchers who are working in problems involved in medical imaging.

Download or read book Multimedia Database Retrieval written by Paisarn Muneesawang and published by Springer. This book was released on 2014-10-25 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores multimedia applications that emerged from computer vision and machine learning technologies. These state-of-the-art applications include MPEG-7, interactive multimedia retrieval, multimodal fusion, annotation, and database re-ranking. The application-oriented approach maximizes reader understanding of this complex field. Established researchers explain the latest developments in multimedia database technology and offer a glimpse of future technologies. The authors emphasize the crucial role of innovation, inspiring users to develop new applications in multimedia technologies such as mobile media, large scale image and video databases, news video and film, forensic image databases and gesture databases. With a strong focus on industrial applications along with an overview of research topics, Multimedia Database Retrieval: Technology and Applications is an indispensable guide for computer scientists, engineers and practitioners involved in the development and use of multimedia systems. It also serves as a secondary text or reference for advanced-level students interested in multimedia technologies.

Download or read book Optimum Path Forest written by Alexandre Xavier Falcao and published by Academic Press. This book was released on 2022-01-06 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Optimum-Path Forest (OPF) classifier was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions. Presents the first book on Optimum-path Forest Shows how it can be used with Deep Learning Gives a wide range of applications Includes the methods, underlying theory and applications of Optimum-Path Forest (OPF)

Download or read book Integrated Region Based Image Retrieval written by James Z. Wang and published by Springer Science & Business Media. This book was released on 2001-05-31 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: The system is exceptionally robust to image alterations such as intensity variation, sharpness variation, intentional distortions, cropping, shifting, and rotation. These features are extremely important to biomedical image databases since visual features in the query image are not exactly the same as the visual features in the images in the database." "Integrated Region-Based Image Retrieval is an excellent reference for researchers in the fields of image retrieval, multimedia, computer vision and image processing."--BOOK JACKET.

Download or read book Semantical Representation and Retrieval of Natural Photographs and Medical Images Using Concept and Context based Feature Spaces written by Md Mahmudur Rahman and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Modeling and Using Context written by Anind Dey and published by Springer Science & Business Media. This book was released on 2005-06-24 with total page 1392 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT 2005, held in Paris, France in July 2005. The 42 revised full papers presented were carefully reviewed and selected from a total of 120 submissions. The papers presented deal with the interdisciplinary topic of modeling and using context from various points of view, ranging through cognitive science, formal logic, artifical intelligence, computational intelligence, philosophical and psychological aspects, and information processing. Highly general philosophical and theoretical issues are complemented by specific applications in various fields.

Download or read book Image Retrieval Using Experience based Relevance Feedback and Visualisation written by and published by . This book was released on 2005 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: