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Image Features and Feature Processing

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Visual Quality Assessment by Machine Learning

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSSIGNAL))

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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.

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Author information

Authors and Affiliations

  1. National Astronomical Observatories, Chinese Academy of Sciences, Beijing, China

    Long Xu

  2. Nanyang Technological University, Singapore, Singapore

    Weisi Lin

  3. University of Southern California, Los Angeles, CA, USA

    C.-C. Jay Kuo

Authors
  1. Long Xu
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  2. Weisi Lin
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  3. C.-C. Jay Kuo
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Correspondence to Long Xu .

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Xu, L., Lin, W., Kuo, CC.J. (2015). Image Features and Feature Processing. In: Visual Quality Assessment by Machine Learning. SpringerBriefs in Electrical and Computer Engineering(). Springer, Singapore. https://doi.org/10.1007/978-981-287-468-9_3

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  • DOI: https://doi.org/10.1007/978-981-287-468-9_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-467-2

  • Online ISBN: 978-981-287-468-9

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