Artificial Intelligence for Maximizing Content Based Image Retrieval

Artificial Intelligence for Maximizing Content Based Image Retrieval

Copyright: © 2009 |Pages: 450

ISBN13: 9781605661742|ISBN10: 1605661740|EISBN13: 9781605661759|ISBN13 Softcover: 9781616925635

DOI: 10.4018/978-1-60566-174-2

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Ma, Zongmin, editor. Artificial Intelligence for Maximizing Content Based Image Retrieval. IGI Global, 2009. https://doi.org/10.4018/978-1-60566-174-2

APA

Ma, Z. (Ed.). (2009). Artificial Intelligence for Maximizing Content Based Image Retrieval. IGI Global. https://doi.org/10.4018/978-1-60566-174-2

Chicago

Ma, Zongmin, ed. Artificial Intelligence for Maximizing Content Based Image Retrieval. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-174-2

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The increasing trend of multimedia data use is likely to accelerate creating an urgent need of providing a clear means of capturing, storing, indexing, retrieving, analyzing, and summarizing data through image data.

Artificial Intelligence for Maximizing Content Based Image Retrieval discusses major aspects of content-based image retrieval (CBIR) using current technologies and applications within the artificial intelligence (AI) field. Providing state-of-the-art research from leading international experts, this book offers a theoretical perspective and practical solutions for academicians, researchers, and industry practitioners.

Table of Contents

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Front Materials

Title Page

Copyright Page

Preface

Acknowledgment

Chapters

Section I

Chapter 1

Genetic Algorithms and Other Approaches in Image Feature Extraction and Representation (pages 1-19)

Danilo Avola, Fernando Ferri, Patrizia Grifoni

The novel technologies used in different application domains allow obtaining digital images with a high complex informative content, which can be exploited to interpret the semantic meaning of the images themselves. Furthermore, it...

Chapter 2

Improving Image Retrieval by Clustering (pages 20-43)

Dany Gebara, Reda Alhajj

This chapter presents a novel approach for content-fbased image retrieval and demonstrates its applicability on non-texture images. The process starts by extracting a feature vector for each image; wavelets are employed in the...

Chapter 3

Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval (pages 44-67)

Gang Zhang, Z. M. Ma, Li Yan

Texture feature extraction and description is one of the important research contents in content-based medical image retrieval. The chapter first proposes a framework of content-based medical image retrieval system. It then analyzes...

Chapter 4

Content-Based Image Classification and Retrieval: A Rule-Based System Using Rough Sets Framework (pages 68-83)

Jafar M. Ali

Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. Thus, it is necessary to develop appropriate information systems to efficiently manage these datasets. Image...

Section II

Chapter 5

Content Based Image Retrieval Using Active-Nets (pages 85-114)

David García Pérez, Antonio Mosquera, Stefano Berretti, Alberto Del Bimbo

Content-based image retrieval has been an active research area in past years. Many different solutions have been proposed to improve performance of retrieval, but the large part of these works have focused on sub-parts of the...

Chapter 6

Content-Based Image Retrieval: From the Object Detection/Recognition Point of View (pages 115-144)

Ming Zhang, Reda Alhajj

Content-Based Image Retrieval (CBIR) aims to search images that are perceptually similar to the querybased on visual content of the images without the help of annotations. The current CBIR systems use global features (e.g., color...

Chapter 7

Making Image Retrieval and Classification More Accurate Using Time Series and Learned Constraints (pages 145-170)

Chotirat “Ann” Ratanamahatana, Eamonn Keogh, Vit Niennattrakul

After the generation of multimedia data turning digital, an explosion of interest in their data storage, retrieval, and processing, has drastically increased in the database and data mining community. This includes videos, images...

Chapter 8

A Machine Learning-Based Model for Content-Based Image Retrieval (pages 171-190)

Hakim Hacid, Abdelkader Djamel Zighed

A multimedia index makes it possible to group data according to similarity criteria. Traditional index structures are based on trees and use the k-Nearest Neighbors (k-NN) approach to retrieve databases. Due to some disadvantages of...

Section III

Chapter 9

Solving the Small and Asymmetric Sampling Problem in the Context of Image Retrieval (pages 192-215)

Ruofei Zhang, Zhongfei (Mark) Zhang

This chapter studies the user relevance feedback in image retrieval. We take this problem as a standard two-class pattern classification problem aiming at refining the retrieval precision by learning through the user relevance...

Chapter 10

Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval (pages 216-234)

Chia-Hung Wei, Chang-Tsun Li

An image is a symbolic representation; people interpret an image and associate semantics with it based on their subjective perceptions, which involves the user’s knowledge, cultural background, personal feelings and so on....

Chapter 11

Preference Extraction in Image Retrieval (pages 235-260)

Pawel Rotter, Andrzej M.J. Skulimowski

In this chapter, we describe two new approaches to content-based image retrieval (CBIR) based on preference information provided by the user interacting with an image search system. First, we present the existing methods of image...

Chapter 12

Personalized Content-Based Image Retrieval (pages 261-287)

Iker Gondra

In content-based image retrieval (CBIR), a set of low-level features are extracted from an image to represent its visual content. Retrieval is performed by image example where a query image is given as input by the user and an...

Section IV

Chapter 13

A Semantics Sensitive Framework of Organization and Retrieval for Multimedia Databases (pages 289-314)

Zhiping Shi, Qingyong Li, Qing He, Zhongzhi Shi

Semantics-based retrieval is a trend of the Content-Based Multimedia Retrieval (CBMR). Typically, in multimedia databases, there exist two kinds of clues for query: perceptive features and semantic classes. In this chapter, we...

Chapter 14

Content-Based Retrieval for Mammograms (pages 315-341)

Chia-Hung Wei, Chang-Tsun Li, Yue Li

As distributed mammogram databases at hospitals and breast screening centers are connected together through PACS, a mammogram retrieval system is needed to help medical professionals locate the mammograms they want to aid in medical...

Chapter 15

Event Detection, Query, and Retrieval for Video Surveillance (pages 342-370)

Ying-li Tian, Arun Hampapur, Lisa Brown, Rogerio Feris, Max Lu, Andrew Senior

Video surveillance automation is used in two key modes: watching for known threats in real-time and searching for events of interest after the fact. Typically, real-time alerting is a localized function, for example, an airport...

Chapter 16

MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework (pages 371-387)

Min Chen, Shu-Ching Chen

This chapter introduces an advanced content-based image retrieval (CBIR) system, MMIR, where Markov model mediator (MMM) and multiple instance learning (MIL) techniques are integrated seamlessly and act coherently as a hierarchical...

Back Materials

Compilation of References

About the Contributors

Index

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