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Cited by 375

The DOI matches its Crossref record; 251 of the 251 references it cites were found in a registry or an index.

From BoW to CNN: Two Decades of Texture Representation for Texture Classification

Open access
Liu, Li, Chen, Jie, Fieguth, Paul, Zhao, Guoying, Chellappa, Rama, Pietikäinen, Matti·2019·International Journal of Computer Vision

Abstract

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.

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Metadata

Authors
Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikäinen, M.
Venue
International Journal of Computer Vision
Published
2018-11-08
Volume
127
Issue
1
Pages
74-109
ISSN
  • 0920-5691 (ISSN-L)
  • 1573-1405
OpenAlex
W2895238724
Cited by
375
References
251
FWCI
23.80
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: From BoW to CNN: Two Decades of Texture Representation for Texture Classification. https://app.cochat.ai/papers/10.1007/s11263-018-1125-z
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Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikäinen, M. (2019). From BoW to CNN: Two Decades of Texture Representation for Texture Classification. International Journal of Computer Vision. https://doi.org/10.1007/s11263-018-1125-z

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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