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Image Indexing and Retrieval based on Hybrid Feature Extraction and Classification Using Image Processing and Machine Learning Algorithms

Baharudin, Baharum (2005) Image Indexing and Retrieval based on Hybrid Feature Extraction and Classification Using Image Processing and Machine Learning Algorithms. PhD thesis, University of Bradford.

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Abstract

Everyday more images are being created, stored and transmitted. However these three acts themselves do not really pose serious problems. The problem becomes apparent when the stored images need to be retrieved. Query using the traditional text-based approaches, though simple and easy to implement, are no longer sufficient when considering the large volume of images that have to be manually labelled. A logical solution to this problem is to search for images based on its content. Thus ContentBased Image Retrieval (CBIR) was born. Since then, many systems have been developed either commercially or in the form of research prototypes. The heart of any CBIR system is feature extraction. In other words features extracted from the images (usually in the form of a vector representation) becomes the index by which the images will be searched. In terms of the number of features used to represent images, it is generally accepted that the use of multiple image features is more desirable than using a single feature. This is evident judging from the major CBIR systems that have been developed. In this thesis, a hybrid feature extraction scheme is proposed based on a combination of features derived from the compressed as well as the pixel domain. By using two well-known classifiers; the Backpropagation Neural Network and Support Vector Machines, the performance of the proposed hybrid feature approach is compared with that of the other feature based approaches which serve as benchmarks. From the results obtained it has been shown that the hybrid feature extraction approach outperforms all the other feature based methods used in the experiments.

Item Type: Thesis (PhD)
Academic Subject : Academic Department - Electrical And Electronics - Instrumentation and Control - Intelligent System - Imaging in intelligent surveillance systems
Subject: T Technology > TJ Mechanical engineering and machinery
Divisions: Engineering > Electrical and Electronic
Depositing User: Users 2053 not found.
Date Deposited: 27 Sep 2013 11:00
Last Modified: 25 Jan 2017 09:46
URI: http://utpedia.utp.edu.my/id/eprint/6925

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