Improving Image Retrieval by Clustering

Improving Image Retrieval by Clustering

Dany Gebara (University of Calgary, Canada) and Reda Alhajj (University of Calgary, Canada)
DOI: 10.4018/978-1-60566-174-2.ch002
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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 process. Then the images (each represented by its feature vector) are classified into groups by employing a density-based clustering approach, namely OPTICS. This highly improves the querying facility by limiting the search space to a single cluster instead of the whole database. The cluster to be searched is determined by applying on the query image the same clustering process OPTICS. This leads to the closest cluster to the query image, and hence, limits the search to the latter cluster without adding the query image to the cluster, except if such request is explicitly specified. The power of this system is demonstrated on non-texture images from the Corel dataset. The achieved results demonstrate that the classification of images is extremely fast and accurate.

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Table of Contents
Zongmin Ma
Chapter 1
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... Sample PDF
Genetic Algorithms and Other Approaches in Image Feature Extraction and Representation
Chapter 2
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... Sample PDF
Improving Image Retrieval by Clustering
Chapter 3
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... Sample PDF
Review on Texture Feature Extraction and Description Methods in Content-Based Medical Image Retrieval
Chapter 4
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... Sample PDF
Content-Based Image Classification and Retrieval: A Rule-Based System Using Rough Sets Framework
Chapter 5
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... Sample PDF
Content Based Image Retrieval Using Active-Nets
Chapter 6
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... Sample PDF
Content-Based Image Retrieval: From the Object Detection/Recognition Point of View
Chapter 7
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... Sample PDF
Making Image Retrieval and Classification More Accurate Using Time Series and Learned Constraints
Chapter 8
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... Sample PDF
A Machine Learning-Based Model for Content-Based Image Retrieval
Chapter 9
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... Sample PDF
Solving the Small and Asymmetric Sampling Problem in the Context of Image Retrieval
Chapter 10
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... Sample PDF
Content Analysis from User's Relevance Feedback for Content-Based Image Retrieval
Chapter 11
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... Sample PDF
Preference Extraction in Image Retrieval
Chapter 12
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... Sample PDF
Personalized Content-Based Image Retrieval
Chapter 13
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... Sample PDF
A Semantics Sensitive Framework of Organization and Retrieval for Multimedia Databases
Chapter 14
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... Sample PDF
Content-Based Retrieval for Mammograms
Chapter 15
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.... Sample PDF
Event Detection, Query, and Retrieval for Video Surveillance
Chapter 16
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... Sample PDF
MMIR: An Advanced Content-Based Image Retrieval System Using a Hierarchical Learning Framework
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