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.

中文翻译：

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图像分析中的特征选择：一项调查

图像分析是一个多产的研究领域，在过去的几十年中得到了广泛的研究，并成功地应用于许多学科。自从大数据出现以来，数字图像的数量呈爆炸式增长，大量的多媒体数据公开可用。不仅需要处理越来越多的图像，还需要知道从中提取哪些特征，特征选择可以在这种情况下提供帮助。本文的目标是调查最近开发和/或应用于图像分析的特征选择方法，涵盖最流行的领域，如图像分类、图像分割等。 最后，使用 well- 对几个流行数据集进行实验评估介绍了已知的特征选择方法，