Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention over several decades. Since 2000, texture representations based on Bag of Words and on Convolutional Neural Networks have been extensively studied with impressive performance. Given this period of remarkable evolution, this paper aims to present a comprehensive survey of advances in texture representation over the last two decades. More than 250 major publications are cited in this survey covering different aspects of the research, including benchmark datasets and state of the art results. In retrospect of what has been achieved so far, the survey discusses open challenges and directions for future research.

中文翻译：

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从 BoW 到 CNN：纹理分类的两个十年纹理表示

纹理是许多类型图像的基本特征，纹理表示是计算机视觉和模式识别中的基本和具有挑战性的问题之一，几十年来引起了广泛的研究关注。自 2000 年以来，基于词袋和卷积神经网络的纹理表示得到了广泛的研究，并取得了令人印象深刻的性能。鉴于这一显着发展时期，本文旨在对过去二十年中纹理表示的进展进行全面调查。本次调查引用了 250 多篇主要出版物，涵盖了研究的不同方面，包括基准数据集和最新成果。回顾迄今为止所取得的成就，该调查讨论了未来研究的开放挑战和方向。