Content Based Image Retrieval
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Author |
: Vipin Tyagi |
Publisher |
: Springer |
Total Pages |
: 399 |
Release |
: 2018-01-15 |
ISBN-10 |
: 9789811067594 |
ISBN-13 |
: 9811067597 |
Rating |
: 4/5 (94 Downloads) |
Synopsis Content-Based Image Retrieval by : Vipin Tyagi
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 |
Total Pages |
: 189 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461509875 |
ISBN-13 |
: 1461509874 |
Rating |
: 4/5 (75 Downloads) |
Synopsis Content-Based Image and Video Retrieval by : Oge Marques
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 |
Total Pages |
: 450 |
Release |
: 2009-01-31 |
ISBN-10 |
: 9781605661759 |
ISBN-13 |
: 1605661759 |
Rating |
: 4/5 (59 Downloads) |
Synopsis Artificial Intelligence for Maximizing Content Based Image Retrieval by : Ma, Zongmin
Discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field.
Author |
: Rajkumar Kannan |
Publisher |
: Springer |
Total Pages |
: 352 |
Release |
: 2012-02-29 |
ISBN-10 |
: 9783642278723 |
ISBN-13 |
: 3642278728 |
Rating |
: 4/5 (23 Downloads) |
Synopsis Data Engineering and Management by : Rajkumar Kannan
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 |
: David Feng |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 494 |
Release |
: 2013-04-17 |
ISBN-10 |
: 9783662053003 |
ISBN-13 |
: 3662053004 |
Rating |
: 4/5 (03 Downloads) |
Synopsis Multimedia Information Retrieval and Management by : David Feng
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 |
: James Z. Wang |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 198 |
Release |
: 2001-05-31 |
ISBN-10 |
: 0792373502 |
ISBN-13 |
: 9780792373506 |
Rating |
: 4/5 (02 Downloads) |
Synopsis Integrated Region-Based Image Retrieval by : James Z. Wang
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 |
: Anindita Mukherjee |
Publisher |
: Mohammed Abdul Sattar |
Total Pages |
: 0 |
Release |
: 2024-01-23 |
ISBN-10 |
: 9798224472000 |
ISBN-13 |
: |
Rating |
: 4/5 (00 Downloads) |
Synopsis Content Based Image Retrieval with Bag of Visual Words by : Anindita Mukherjee
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 |
: Suresh Chandra Satapathy |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 553 |
Release |
: 2013-10-05 |
ISBN-10 |
: 9783319029313 |
ISBN-13 |
: 3319029312 |
Rating |
: 4/5 (13 Downloads) |
Synopsis Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013 by : Suresh Chandra Satapathy
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 |
: Björn Barz |
Publisher |
: Cuvillier Verlag |
Total Pages |
: 322 |
Release |
: 2020-12-23 |
ISBN-10 |
: 9783736963467 |
ISBN-13 |
: 3736963467 |
Rating |
: 4/5 (67 Downloads) |
Synopsis Semantic and Interactive Content-based Image Retrieval by : Björn Barz
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 |
: Jean-Francois Boulicaut |
Publisher |
: Springer |
Total Pages |
: 597 |
Release |
: 2004-11-05 |
ISBN-10 |
: 9783540301158 |
ISBN-13 |
: 3540301151 |
Rating |
: 4/5 (58 Downloads) |
Synopsis Machine Learning: ECML 2004 by : Jean-Francois Boulicaut
The proceedings of ECML/PKDD 2004 are published in two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac- ne Learning (LNAI 3201) and the Proceedings of the 8th European Conferences on Principles and Practice of Knowledge Discovery in Databases (LNAI 3202). The two conferences were co-located in Pisa, Tuscany, Italy during September 20–24, 2004. It was the fourth time in a row that ECML and PKDD were co-located. - ter the successful co-locations in Freiburg (2001), Helsinki (2002), and Cavtat- Dubrovnik (2003), it became clear that researchersstrongly supported the or- nization of a major scienti?c event about machine learning and data mining in Europe. We are happy to provide some statistics about the conferences. 581 di?erent papers were submitted to ECML/PKDD (about a 75% increase over 2003); 280 weresubmittedtoECML2004only,194weresubmittedtoPKDD2004only,and 107weresubmitted to both.Aroundhalfofthe authorsforsubmitted papersare from outside Europe, which is a clear indicator of the increasing attractiveness of ECML/PKDD. The Program Committee members were deeply involved in what turned out to be a highly competitive selection process. We assigned each paper to 3 - viewers, deciding on the appropriate PC for papers submitted to both ECML and PKDD. As a result, ECML PC members reviewed 312 papers and PKDD PC members reviewed 269 papers. We accepted for publication regular papers (45 for ECML 2004 and 39 for PKDD 2004) and short papers that were as- ciated with poster presentations (6 for ECML 2004 and 9 for PKDD 2004). The globalacceptance ratewas14.5%for regular papers(17% if we include the short papers).