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Feature Selection and Heterogeneous Descriptors

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Image Registration

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

While the focus in Chap. 5 was on descriptors that were made up of homogeneous features, the focus in this chapter is on descriptors that are composed of features of different types. Heterogeneous descriptors are created from a combination of various types of features as described in Chap. 4. The features are selected in such a way that the combined feature set delivers the most information about an image or sub-image. Also discussed in this chapter are various feature-selection methods that choose the optimal or suboptimal feature set from a large number of features. Both filter and wrapper feature selection algorithms are discussed, including max-min, sequential forward selection, sequential backward selection, plus l take away r, and branch and bound algorithms.

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Authors and Affiliations

  1. Dept. Computer Science and Engineering, 303 Russ Engineering Center, Wright State University, Dayton, OH, USA

    Prof. A. Ardeshir Goshtasby

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  1. Prof. A. Ardeshir Goshtasby
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Correspondence to A. Ardeshir Goshtasby .

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Goshtasby, A.A. (2012). Feature Selection and Heterogeneous Descriptors. In: Image Registration. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-2458-0_6

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  • DOI: https://doi.org/10.1007/978-1-4471-2458-0_6

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2457-3

  • Online ISBN: 978-1-4471-2458-0

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