Improving High-dimensional Indexing for Content-based Image Retrieval
Author: Jui-Che Teng
Publisher:
Published: 2002
Total Pages: 153
ISBN-13:
DOWNLOAD EBOOKRead and Download eBook Full
Author: Jui-Che Teng
Publisher:
Published: 2002
Total Pages: 153
ISBN-13:
DOWNLOAD EBOOKAuthor: Peggy Agouris
Publisher: Springer
Published: 2003-06-26
Total Pages: 319
ISBN-13: 3540466215
DOWNLOAD EBOOKThis book constitutes the thoroughly refereed post-proceedings of the International Workshop on Integrated Databases, Digital Images and GIS, ISD'99, held in Portland, Maine, USA in June 1999. The 18 revised full papers presented went through a double reviewing process and were selected from nearly 40 original submissions. The book is divided into parts on object extraction from raster images, geospatial analysis, formalisms and modeling, and data access.
Author: Dr. A. Hariprasad Reddy and Dr. N. Subhash Chandra
Publisher: Lulu Publication
Published: 2021-04-21
Total Pages: 112
ISBN-13: 1667178393
DOWNLOAD EBOOKAuthor: Michael Arthur Schuh
Publisher:
Published: 2015
Total Pages: 131
ISBN-13:
DOWNLOAD EBOOKThe indexing of high-dimensional data remains a challenging task amidst an active and storied area of computer science research that impacts many far-reaching applications. At the crossroads of databases and machine learning, modern data indexing enables information retrieval capabilities that would otherwise be impractical or near impossible to attain and apply. One such useful retrieval task in our increasingly data-driven world is the k-nearest neighbor (k-NN) search, which returns the k most similar items in a dataset to the search query provided. While the k-NN concept was popularized in every-day use through the sorted (ranked) results of online text-based search engines like Google, multimedia applications are rapidly becoming the new frontier of research. This dissertation advances the current state of high-dimensional data indexing with the creation of a novel index named ID* (\ID Star"). Based on extensive theoretical and empirical analyses, we discuss important challenges associated with high dimensional data and identify several shortcomings of existing indexing approaches and methodologies. By further mitigating against the negative effects of the curse of dimensionality, we are able to push the boundary of effective k-NN retrieval to a higher number of dimensions over much larger volumes of data. As the foundations of the ID* index, we developed an open-source and extensible distance-based indexing framework predicated on the basic concepts of the popular iDistance index, which utilizes an internal B+-tree for efficient one-dimensional data indexing. Through the addition of several new heuristic-guided algorithmic improvements and hybrid indexing extensions, we show that our new ID* index can perform significantly better than several other popular alternative indexing techniques over a wide variety of synthetic and real-world data. In addition, we present applications of our ID* index through the use of k-NN queries in Content-Based Image Retrieval (CBIR) systems and machine learning classification. An emphasis is placed on the NASA sponsored interdisciplinary research goal of developing a CBIR system for large-scale solar image repositories. Since such applications rely on fast and effective k-NN queries over increasingly large-scale and high-dimensional datasets, it is imperative to utilize an efficient data indexing strategy such as the ID* index.
Author: Cui Yu
Publisher: Springer
Published: 2003-08-01
Total Pages: 159
ISBN-13: 3540457704
DOWNLOAD EBOOKIn this monograph, we study the problem of high-dimensional indexing and systematically introduce two efficient index structures: one for range queries and the other for similarity queries. Extensive experiments and comparison studies are conducted to demonstrate the superiority of the proposed indexing methods. Many new database applications, such as multimedia databases or stock price information systems, transform important features or properties of data objects into high-dimensional points. Searching for objects based on these features is thus a search of points in this feature space. To support efficient retrieval in such high-dimensional databases, indexes are required to prune the search space. Indexes for low-dimensional databases are well studied, whereas most of these application specific indexes are not scaleable with the number of dimensions, and they are not designed to support similarity searches and high-dimensional joins.
Author: Paul Clough
Publisher: Springer Science & Business Media
Published: 2011-04-06
Total Pages: 821
ISBN-13: 3642201601
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 33rd annual European Conference on Information Retrieval Research, ECIR 2011, held in Dublin, Ireland, in April 2010. The 45 revised full papers presented together with 24 poster papers, 17 short papers, and 6 tool demonstrations were carefully reviewed and selected from 223 full research paper submissions and 64 poster/demo submissions. The papers are organized in topical sections on text categorization, recommender systems, Web IR, IR evaluation, IR for Social Networks, cross-language IR, IR theory, multimedia IR, IR applications, interactive IR, and question answering /NLP.
Author: Björn Barz
Publisher: Cuvillier Verlag
Published: 2020-12-23
Total Pages: 322
ISBN-13: 3736963467
DOWNLOAD EBOOKContent-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: Ma, Zongmin
Publisher: IGI Global
Published: 2009-01-31
Total Pages: 450
ISBN-13: 1605661759
DOWNLOAD EBOOKDiscusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field.
Author: Basant Tiwari
Publisher: Springer
Published: 2018-04-18
Total Pages: 657
ISBN-13: 9811081980
DOWNLOAD EBOOKThe book is a compilation of best papers presented at International Conference on Recent Advancement in Computer and Communication (ICRAC 2017) organized by IMPLab Research and Innovation Foundation, Bhopal, India. The book covers all aspects of computers and communication techniques including pervasive computing, distributed computing, cloud computing, sensor and adhoc network, image, text and speech processing, pattern recognition and pattern analysis, digital signal processing, digital electronics, telecommunication technologies, robotics, VLSI technologies, embedded system, satellite communication, digital signal processing, and digital communication. The papers included are original research works of experts from industry, government centers and academic institutions; experienced in engineering, design and research.
Author: Maristella Agosti
Publisher: Springer
Published: 2003-05-15
Total Pages: 320
ISBN-13: 3540453687
DOWNLOAD EBOOKInformation Retrieval (IR) is concerned with the effective and efficient retrieval of information based on its semantic content. The central problem in IR is the quest to find the set of relevant documents, among a large collection containing the information sought, satisfying a user's information need usually expressed in a natural language query. Documents may be objects or items in any medium: text, image, audio, or indeed a mixture of all three. This book presents 12 revised lectures given at the Third European Summer School in Information Retrieval, ESSIR 2000, held at the Villa Monastero, Varenna, Italy, in September 2000. The first part of the book is devoted to the foundation of IR and related areas; the second part on advanced topics addresses various current issues, from usability aspects to Web searching and browsing.