From BoW to CNN: Two Decades of Texture Representation for Texture Classification
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
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从 BoW 到 CNN：纹理分类的纹理表示的两个十年
DOI：
10.1007/s11263-018-1125-z
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发表时间：
2019-01-01
影响因子：
19.5
通讯作者：
Pietikainen, Matti
中科院分区：
文献类型：
--
作者：
Liu, Li;Chen, Jie;Pietikainen, Matti
纹理是许多图像的基本特征，纹理表示是计算机视觉和模式识别中的一个重要而又具有挑战性的问题，在过去的几十年里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已经得到了广泛的研究，并取得了令人印象深刻的性能。鉴于这一时期的显着演变，本文的目的是提出一个全面的调查在过去二十年的纹理表示的进展。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先进的结果。回顾迄今为止所取得的成就，调查讨论了开放的挑战和未来研究的方向。
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.