期刊
ARTIFICIAL INTELLIGENCE REVIEW
卷 53, 期 4, 页码 2905-2931出版社
SPRINGER
DOI: 10.1007/s10462-019-09750-3
关键词
Feature selection; Image analysis; Pattern recognition; High dimensionality; Image datasets
资金
- European Union FEDER funds
- Spanish Ministerio de Economia y Competitividad [TIN2015-65069-C2]
- Conselleria de Industria of the Xunta de Galicia [GRC2014/035]
- Principado de Asturias [IDI-2018-000176]
- Xunta de Galicia (Centro singular de investigacion de Galicia accreditation 2016-2019) [ED431G/01]
- European Union (European Regional Development Fund-ERDF) [ED431G/01]
Image analysis is a prolific field of research which has been broadly studied in the last decades, successfully applied to a great number of disciplines. Since the apparition of Big Data, the number of digital images is explosively growing, and a large amount of multimedia data is publicly available. Not only is it necessary to deal with this increasing number of images, but also to know which features extract from them, and feature selection can help in this scenario. The goal of this paper is to survey the most recent feature selection methods developed and/or applied to image analysis, covering the most popular fields such as image classification, image segmentation, etc. Finally, an experimental evaluation on several popular datasets using well-known feature selection methods is presented, bearing in mind that the aim is not to provide the best feature selection method, but to facilitate comparative studies for the research community.
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