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Visual Quality Assessment by Machine LearningImage Features and Feature Processing

Visual Quality Assessment by Machine Learning: Image Features and Feature Processing [Image representation is an elementary problem in any image processing application. The straightforward method is to represent an image by point-to-point. Regarding biological tasks of image processing, such as recognition, retrieval, tracking, and categorizing, such a method would be very uneconomical. The neighboring points are highly correlated with each other in natural images, so there exists a large amount of redundancies in natural images. The biological image processing should compress these redundancies as much as possible, which would significantly benefit the following classification, recognition, or retrieval tasks. To achieve this goal, pictorial information should be processed in such a way that the highest possible proportion of redundant information is filtered out. In this chapter, we first summarize the state-of-the-art processings of image representation by arranging them into basic processing and advanced processing categories, resulting in basic features and advanced features, respectively. In addition, feature learning is investigated to generate more efficient features for biological image-processing tasks. The feature selection and feature extraction techniques are used in feature learning.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Visual Quality Assessment by Machine LearningImage Features and Feature Processing

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/lp/springer-journals/visual-quality-assessment-by-machine-learning-image-features-and-pz0pMJlyhp
Publisher
Springer Singapore
Copyright
© The Author(s) 2015
ISBN
978-981-287-467-2
Pages
37 –65
DOI
10.1007/978-981-287-468-9_3
Publisher site
See Chapter on Publisher Site

Abstract

[Image representation is an elementary problem in any image processing application. The straightforward method is to represent an image by point-to-point. Regarding biological tasks of image processing, such as recognition, retrieval, tracking, and categorizing, such a method would be very uneconomical. The neighboring points are highly correlated with each other in natural images, so there exists a large amount of redundancies in natural images. The biological image processing should compress these redundancies as much as possible, which would significantly benefit the following classification, recognition, or retrieval tasks. To achieve this goal, pictorial information should be processed in such a way that the highest possible proportion of redundant information is filtered out. In this chapter, we first summarize the state-of-the-art processings of image representation by arranging them into basic processing and advanced processing categories, resulting in basic features and advanced features, respectively. In addition, feature learning is investigated to generate more efficient features for biological image-processing tasks. The feature selection and feature extraction techniques are used in feature learning.]

Published: May 10, 2015

Keywords: Image feature; Feature learning; Feature selection; Feature detector

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