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Author: Yixin Chen
Publisher: Springer Science & Business Media
ISBN: 1402080352
Category : Science
Languages : en
Pages : 194
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Book Description
In the early 1990s, the establishment of the Internet brought forth a revolutionary viewpoint of information storage, distribution, and processing: the World Wide Web is becoming an enormous and expanding distributed digital library. Along with the development of the Web, image indexing and retrieval have grown into research areas sharing a vision of intelligent agents. Far beyond Web searching, image indexing and retrieval can potentially be applied to many other areas, including biomedicine, space science, biometric identification, digital libraries, the military, education, commerce, culture and entertainment. Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results. The topics of this book reflect authors' experiences of machine learning and statistical modeling based image indexing and retrieval. This book contains detailed references for further reading and research in this field as well.
Author: Yixin Chen
Publisher: Springer Science & Business Media
ISBN: 1402080352
Category : Science
Languages : en
Pages : 194
Get Book Here
Book Description
In the early 1990s, the establishment of the Internet brought forth a revolutionary viewpoint of information storage, distribution, and processing: the World Wide Web is becoming an enormous and expanding distributed digital library. Along with the development of the Web, image indexing and retrieval have grown into research areas sharing a vision of intelligent agents. Far beyond Web searching, image indexing and retrieval can potentially be applied to many other areas, including biomedicine, space science, biometric identification, digital libraries, the military, education, commerce, culture and entertainment. Machine Learning and Statistical Modeling Approaches to Image Retrieval describes several approaches of integrating machine learning and statistical modeling into an image retrieval and indexing system that demonstrates promising results. The topics of this book reflect authors' experiences of machine learning and statistical modeling based image indexing and retrieval. This book contains detailed references for further reading and research in this field as well.
Author: Lining Zhang
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659635571
Category : Computer vision
Languages : en
Pages : 208
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Book Description
With the rapid popularization of digital cameras and mobile phone cameras, we have witnessed an explosive growth in the size of digital image collections. Image retrieval, which is an area of the study concerned with searching and browsing images from a large-scale image database, has attracted much attention among researchers in the fields of image processing, computer vision, and database management both from the academia and industry during the last decades. In this book, we will show the problems of this area and discuss the possible solutions to these problems.
Author: Ma, Zongmin
Publisher: IGI Global
ISBN: 1605661759
Category : Computers
Languages : en
Pages : 450
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Book Description
Discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field.
Author: Dr.Raghavender K.V
Publisher: Archers & Elevators Publishing House
ISBN: 8119385306
Category : Antiques & Collectibles
Languages : en
Pages : 55
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Book Description
Author: Oge Marques
Publisher: Springer Science & Business Media
ISBN: 1461509874
Category : Computers
Languages : en
Pages : 189
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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: Anshuman Vikram Singh
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 68
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Book Description
"A content-based image retrieval (CBIR) system works on the low-level visual features of a user input query image, which makes it difficult for the users to formulate the query and also does not give satisfactory retrieval results. In the past image annotation was proposed as the best possible system for CBIR which works on the principle of automatically assigning keywords to images that help image retrieval users to query images based on these keywords. Image annotation is often regarded as the problem of image classification where images are represented by some low-level features an teh mapping between low-level features and high-level concepts (class labels) is done by supervised learning algorithms. In a CBIR system learning of effective feature representations and similarity measures is very important for the retrieval performance. Semantic gap has been the key challenge for this problem. A semantic gap exists between low-level image pixels captured by machines and the high-level semantics perceived by humans. The recent successes of deep learning techniques especially Convolutional Neural Networks (CNN) in solving computer vision applications has inspired me to work on this thesis so as to solve teh problem of CBIR using a dataset of annotated images."--Abstract.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The ever increasing amount of digital information has created a need for effective information retrieval systems. As it is said, information which cannot be found easily is as good as lost. As information comes in various formats and types, their retrieval mechanisms also need to differ correspondingly. In this work, we deal with the task of content based image retrieval, in which the system facilitates the interaction between a user and an image database by automatic analysis of the image content. We know about the ambiguities that can exist even in the simplest of phrases in a natural language. As an example, a sentence such as "Flying planes can be dangerous" can either mean "Flying planes are dangerous" or "Flying planes is dangerous". Images are no different. In fact, the old saying "A picture is worth a thousand words" is as true here as it is anywhere. In this work, we take the view that these ambiguities are natural, and thus the system should not take the one or the other viewpoint from the very beginning. The earliest systems for image retrieval allowed the user to specify his or her viewpoint by giving access to its internal system parameters, which can be complicated or tiring for the user. A modern system, on the other hand, seeks to learn this viewpoint by imposing less responsibilities on the user. This can be achieved using relevance feedback, in which the user progressively gives the system more and more information, in return for better results. Relevance feedback can be short-term, in which the data collected is discarded as soon as the session is over, and long-term, in which the data can be collected over multiple sessions of one user, or even over multiple users. In this work, however, we will constrain ourselves to short-term relevance feedback, as in our view the ambiguities or the multiple interpretations present in an image cannot be handled otherwise. The later part of the thesis delves into image search as an offshoot of the traditional t.
Author: James Z. Wang
Publisher: Springer Science & Business Media
ISBN: 9780792373506
Category : Computers
Languages : en
Pages : 198
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Book Description
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.
Author: Björn Barz
Publisher: Cuvillier Verlag
ISBN: 3736963467
Category : Computers
Languages : en
Pages : 322
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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: Peter Enser
Publisher: Springer Science & Business Media
ISBN: 3540225390
Category : Computers
Languages : en
Pages : 694
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Book Description
This book constitutes the refereed proceedings of the Third International Conference on Image and Video Retrieval, CIVR 2004, held in Dublin, Ireland in July 2004. The 31 revised full papers and 44 poster papers presented were carefully reviewed and selected from 125 submissions. The papers are organized in topical sections on image annotation and user searching, image and video retrieval algorithms, person and event identification for retrieval, content-based image and video retrieval, and user perspectives.
