Visual information retrieval using Java and LIRE

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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 relev...

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Bibliographic Details
Main Author: Lux, Mathias
Other Authors: Marques, Oge
Format: eBook
Language:English
Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2013.
Series:Synthesis digital library of engineering and computer science.
Synthesis lectures on information concepts, retrieval, and services ; # 25.
Subjects:
Image processing > Digital techniques.
Java (Computer program language)
Lucene Image REtrieval.
Picture archiving and communication systems.
image processing
image retrieval
image search
indexing
information retrieval
Java
visual descriptors
visual search
Online Access:Abstract with links to full text
Table of Contents:
  • Preface
  • Acknowledgments
  • 1. Introduction
  • 1.1 Design challenges
  • 1.2 Getting started with LIRE
  • 1.2.1 Java setup
  • 1.2.2 Downloading, unpacking, and running LireDemo
  • 1.2.3 Indexing an image collection
  • 1.2.4 Browsing the index, selecting an image, and performing a search
  • 2. Information retrieval: selected concepts and techniques
  • 2.1 Basic concepts and document representation
  • 2.1.1 Vector retrieval model
  • 2.2 Retrieval evaluation
  • 2.3 Text information retrieval with Lucene
  • 3. Visual features
  • 3.1 Digital imaging in a nutshell
  • 3.1.1 Digital imaging in Java
  • 3.2 Global features
  • 3.2.1 Color features
  • 3.2.2 Texture features
  • 3.2.3 Combining color and texture
  • 3.3 Local features
  • 3.3.1 Scale-invariant feature transform (SIFT)
  • 3.3.2 Speeded-up robust features (SURF)
  • 3.4 Metrics, normalization, and distance functions
  • 3.5 Evaluation of visual features
  • 3.5.1 Figures of merit
  • 3.5.2 Datasets
  • 3.5.3 Challenges
  • 3.6 Feature extraction using LIRE
  • 4. Indexing visual features
  • 4.1 Indexing: the na�ive approach
  • 4.1.1 Basic indexing and linear search in LIRE
  • 4.2 Nearest-neighbor search
  • 4.3 Hashing
  • 4.3.1 Locality sensitive hashing
  • 4.3.2 Metric spaces approximate indexing
  • 4.4 Bag of visual words
  • 4.4.1 Bag of visual words using LIRE
  • 5. LIRE: an extensible Java CBIR library
  • 5.1 Architecture and low-level features
  • 5.2 Indexing and searching
  • 5.3 Advanced features
  • 5.3.1 Bag of visual words
  • 5.3.2 Result re-ranking and filtering
  • 5.4 How to apply LIRE
  • 5.4.1 Scenario investigation
  • 5.4.2 Benchmarking
  • 5.4.3 Deployment tests and performance optimization
  • 6. Concluding remarks
  • 6.1 Research directions, challenges, and opportunities
  • 6.2 Resources
  • Bibliography
  • Authors' biographies.

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