Content Based Image Retrieval Using Visual Features
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Author |
: Independently Published |
Publisher |
: |
Total Pages |
: 44 |
Release |
: 2017-08-08 |
ISBN-10 |
: 1726752321 |
ISBN-13 |
: 9781726752329 |
Rating |
: 4/5 (21 Downloads) |
Synopsis Content Based Image Retrieval Using Visual Features by : Independently Published
Present day applications require various kinds of images and pictures as a source of information for interpretation and analysis .Recently, digital content has become a significant and inevitable asset for any enterprise and the need for visual content management is on the rise as well. There has been an increase in attention towards the automated management and retrieval of digital images owing to the drastic development in the number and size of image databases. A significant and increasingly popular approach that aids in the retrieval of image data from a huge collection is called Content-based image retrieval (CBIR). Content-based image retrieval has attracted voluminous research in the last decade paving way for development of numerous techniques and systems besides creating interest on fields that support these systems. CBIR indexes the images based on the features obtained from visual content so as to facilitate speedy retrieval. This report discusses the state of the art of the content based image retrieval highlighting the main components and reviewing various approaches of CBIR focusing on efficiency of retrieval.
Author |
: Lux Mathias |
Publisher |
: Springer Nature |
Total Pages |
: 96 |
Release |
: 2022-05-31 |
ISBN-10 |
: 9783031022821 |
ISBN-13 |
: 3031022823 |
Rating |
: 4/5 (21 Downloads) |
Synopsis Visual Information Retrieval Using Java and LIRE by : Lux Mathias
Visual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) from large, unstructured repositories. The goal of VIR is to retrieve matches ranked by their relevance to a given query, which is often expressed as an example image and/or a series of keywords. During its early years (1995-2000), the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. During the past decade, it was widely recognized that the challenges imposed by the lack of coincidence between an image's visual contents and its semantic interpretation, also known as semantic gap, required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on. In this introductory book, we focus on a subset of VIR problems where the media consists of images, and the indexing and retrieval methods are based on the pixel contents of those images -- an approach known as content-based image retrieval (CBIR). We present an implementation-oriented overview of CBIR concepts, techniques, algorithms, and figures of merit. Most chapters are supported by examples written in Java, using Lucene (an open-source Java-based indexing and search implementation) and LIRE (Lucene Image REtrieval), an open-source Java-based library for CBIR. Table of Contents: Introduction / Information Retrieval: Selected Concepts and Techniques / Visual Features / Indexing Visual Features / LIRE: An Extensible Java CBIR Library / Concluding Remarks
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 |
: IEEE Staff |
Publisher |
: |
Total Pages |
: |
Release |
: 2019-07-16 |
ISBN-10 |
: 1728121787 |
ISBN-13 |
: 9781728121789 |
Rating |
: 4/5 (87 Downloads) |
Synopsis 2019 IEEE International Conference on Signals and Systems (ICSigSys) by : IEEE Staff
Communications Networking Internet of Things Big Data and Multimedia MIMO and Array Signal Processing Instrumentations and Measurements Radar and Sonar Remote Sensing Biomedics Control, Robotics and Automations Computer Vision Security and Forensics Audio Speech Processing Image Video Processing Adaptive Signal Processing and Systems Statistical Signal Processing Design and Implementation of Signal Processing Systems (Embedded Systems, DSP, ASIC, VLSI and FPGA)
Author |
: Fouad Sabry |
Publisher |
: One Billion Knowledgeable |
Total Pages |
: 91 |
Release |
: 2024-05-09 |
ISBN-10 |
: PKEY:6610000566457 |
ISBN-13 |
: |
Rating |
: 4/5 (57 Downloads) |
Synopsis Content Based Image Retrieval by : Fouad Sabry
What is Content Based Image Retrieval Content-based image retrieval, also known as query by image content and content-based visual information retrieval (CBVIR), is the application of computer vision techniques to the problem of image retrieval, which is the difficulty of searching for digital images in big databases. Other names for this technique include content-based visual information retriev. In contrast to the conventional concept-based methods, content-based picture retrieval is a more recent development. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Content-based image retrieval Chapter 2: Information retrieval Chapter 3: Image retrieval Chapter 4: Automatic image annotation Chapter 5: Tag cloud Chapter 6: Video search engine Chapter 7: Image organizer Chapter 8: Image meta search Chapter 9: Reverse image search Chapter 10: Visual search engine (II) Answering the public top questions about content based image retrieval. (III) Real world examples for the usage of content based image retrieval in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Content Based Image Retrieval.
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 |
: 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 |
: IEEE Staff |
Publisher |
: |
Total Pages |
: |
Release |
: 2017-06-15 |
ISBN-10 |
: 1538639017 |
ISBN-13 |
: 9781538639016 |
Rating |
: 4/5 (17 Downloads) |
Synopsis 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) by : IEEE Staff
ICICCS 2017 will provide an outstanding international forum for scientists from all over the world to share ideas and achievements in the theory and practice of all areas of inventive systems which includes control, artificial intelligence, automation systems, computing systems, electronics systems, electrical and informative systems etc Presentations should highlight computing methodologies as a concept that combines theoretical research and applications in automation, information and computing technologies All aspects of intelligent computing and control systems are of interest theory, algorithms, tools, applications, etc
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 |
: 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.