Last edited by Kezil
Thursday, October 15, 2020 | History

2 edition of Visual Indexing and Retrieval found in the catalog.

Visual Indexing and Retrieval

by Jenny Benois-Pineau

  • 314 Want to read
  • 17 Currently reading

Published by Springer New York in New York, NY .
Written in English

    Subjects:
  • Information systems,
  • Information organization,
  • Computer networks,
  • Information Systems and Communication Service,
  • Information retrieval,
  • Information Systems Applications (incl. Internet),
  • Computer science,
  • Information storage and retrieval systems,
  • Multimedia systems

  • Edition Notes

    Statementby Jenny Benois-Pineau, Frédéric Precioso, Matthieu Cord
    SeriesSpringerBriefs in Computer Science
    ContributionsPrecioso, Frédéric, Cord, Matthieu, SpringerLink (Online service)
    The Physical Object
    Format[electronic resource] /
    ID Numbers
    Open LibraryOL27094690M
    ISBN 109781461435884

    Bibliographic details on Coherent Semantic-Visual Indexing for Large-Scale Image Retrieval in the Cloud. Information retrieval typically assumes a static or relatively static database against which people search. Search engine companies construct these databases by sending out “spiders” and then indexing the Web pages they find. By contrast, information filtering supports people in the passive monitoring for desired information.

    In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model can be applied to image classification, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a. Introduction to Image Indexing and Retrieval zImage representation zA visual content descriptor can be either global or local. zThe global descriptor uses the visual features of the whole image zA local descriptor uses the visual features of regions or objects to describe the image content, with the aid of region/object segmentation techniques.

    many existing content-based visual retrieval algorithms and systems leverage the classic inverted file structure to index large scale visual database for scalable retrieval. Mean-while, some hashing based techniques are also proposed for indexing in a similar perspective. To achieve this goal, visual codebook learning and feature quantization. A novel method for content-based image retrieval based upon combined feature vectors of shape, texture, and color similarities has been suggested. In addition, an image specific color reduction method has been introduced, which allows a bit JPEG image to be shown in .


Share this book
You might also like
British trade unions, 1875-1933

British trade unions, 1875-1933

Meet the presidents

Meet the presidents

Clothing

Clothing

ENTERPRISE reference guide

ENTERPRISE reference guide

The 2000 Import and Export Market for Building and Monumental Stone in Cyprus

The 2000 Import and Export Market for Building and Monumental Stone in Cyprus

child Bach

child Bach

The Princess and the Frog (Well Loved Tales)

The Princess and the Frog (Well Loved Tales)

Best Seat in House

Best Seat in House

Texas traffic data acquisition program

Texas traffic data acquisition program

The political philosophy of William Blake

The political philosophy of William Blake

Japan directory 1987.

Japan directory 1987.

Meg and Mog

Meg and Mog

modern state.

modern state.

Complete Concepts

Complete Concepts

Dahlias cargo

Dahlias cargo

Visual Indexing and Retrieval by Jenny Benois-Pineau Download PDF EPUB FB2

This book presents the most recent results and important trends in visual information indexing and retrieval. It is intended for young researchers, as well as, professionals looking for.

This book provides a deep analysis and wide coverage of the very strong trend in computer vision and visual indexing and retrieval, covering such topics as incorporation of models of Human Visual attention into analysis and retrieval tasks.

It makes the bridge between psycho-visual modelling of. The research in content-based indexing and retrieval of visual information such as images and video has become one of the most populated directions in the vast area of information technologies. The areas of societal activity, such as, video protection and security, also generate thousands and thousands of terabytes of visual content.

This book provides a deep analysis and wide coverage of the very strong trend in computer vision and visual indexing and retrieval, covering such topics as incorporation of models of Human Visual attention into analysis and retrieval tasks. 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).Reviews: 2.

Therefore, the representation, indexing and search techniques of image and video data has been a longstanding focus in research fields such as computer vision, multimedia analysis, and information retrieval, while serving as an emerging demand to improve the multimedia search services for big companies such as Google and Yahoo!.

Through mass-digitization projects and with the use of OCR technologies, digitized books are becoming available on the Web and in digital libraries. The unprecedented scale of these efforts, the unique characteristics of the digitized material as well as the unexplored possibilities of user interactions make full-text book search an exciting area of information retrieval (IR) [ ].

LIRE: Lucene Image Retrieval LIRE is a Java library that provides a simple way to retrieve images and photos based on color and texture characteristics. LIRE creates a Lucene index of image features for content based image retrieval (CBIR) using local and global state-of-the-art methods.

Introduction to Information Retrieval. This is the companion website for the following book. Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. You can order this book at CUP, at your local bookstore or on the best search term to use is the ISBN: Index construction.

Hardware basics; Blocked sort-based indexing; Single-pass in-memory indexing; Distributed indexing; Dynamic indexing; Other types of indexes; References and further reading.

Index compression. Statistical properties of terms in information retrieval. Heaps' law: Estimating the number of terms; Zipf's law: Modeling the. In indexing decisions, concepts are recorded as data elements organised into easily accessible forms for retrieval.

These records can appear in various forms, e.g. back-of-the-book indexes, indexes to catalogues and bibliographies, machine files, etc.

The process of indexing has a close resemblance to the search process. Abstract. 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.

Information Retrieval System Notes Pdf – IRS Notes Pdf book starts with the topics Classes of automatic indexing, Statistical indexing.

Natural language, Concept indexing, Hypertext linkages,Multimedia Information Retrieval – Models and Languages – Data Modeling, Query Languages, lndexingand Searching. Here we view video retrieval from a different angle.

We seek to construct a video Index to suit various users' needs. However, constructing a video Index is far more complex than constructing an index for books. For books, the form of an index is fixed (e.g., key words).

For videos, the viewer's interests may cover a wide range. The business community is taking note of the advances made in visual information indexing and retrieval and investing in the field.

Of course nothing propels a technology to new heights like an injection of money, and we need look no further than the Internet and the World Wide Web to find examples of technologies where potential for business.

An efficient radix trie‐based semantic visual indexing model for large‐scale image retrieval in cloud environment. Krishnaraj.

Department of Computer Science and Engineering, SASI Institute of Technology & Engineering, Tadepalligudem, India. Search for more papers by this author. High-Level Features for Image Indexing and Retrieval: /ch To Support Customers in Easily and Affordably Obtaining the Latest Peer-Reviewed Research, Receive a 20% Discount on ALL Publications and Free Worldwide Shipping on Orders Over US$ Additionally, Enjoy an Additional 5% Pre-Publication Discount on all.

3D Object Processing: Compression, Indexing and Watermarking is an invaluable resource for graduate students and researchers working in signal and image processing, computer aided design, animation and imaging systems.

Practising engineers who want to expand their knowledge of 3D video objects, including data compression, indexing, security, and copyrighting of information, will also find this.

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 image retrieval problem, that is, the problem of searching for digital images in large databases (see this survey for a recent scientific overview of the CBIR field).

Content-based image retrieval is opposed to. SCHATZ, B. Information retrieval in digital libraries: Bringing search to the net. Science]] Google Scholar Cross Ref; SCHAUBLE, P.

Multimedia Information Retrieval: Content-Based Information Retrieval from Large Text and Audio Databases. Kluwer Academic Publishers, Hingham, MA.]] Google Scholar Digital Library.

Abstract: Visual surveillance produces large amounts of video data. Effective indexing and retrieval from surveillance video databases are very important.

Although there are many ways to represent the content of video clips in current video retrieval algorithms, there still exists a semantic gap between users and retrieval systems.3 Dictionaries and tolerant retrieval 45 Search structures for dictionaries 45 Wildcard queries 48 Spelling correction 52 Phonetic correction 58 References and further reading 59 4 Index construction 61 Hardware basics 62 Blocked sort-based indexing 63 Single-pass in-memory indexing 66 Distributed indexing   A large body of research indicates that visual cues help us to better retrieve and remember information.

The research outcomes on visual learning make complete sense when you consider that our.