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

By Li Liu, Jie Chen, Paul W. Fieguth, Guoying Zhao, Ramalingam Chellappa, Matti Pietikäinen. International Journal of Computer Vision, 2018

360 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9175e4f461aaaddc87072e2b1451c8da7fdff7bb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Ran He
2. Qixiang Ye
3. Li Liu
4. Zhengjun Zha
5. Qi Tian
6. Zhenan Sun
7. Jianbin Jiao
8. Yang Cao

Citations in the corpus

• Min-Entropy Latent Model for Weakly Supervised Object Detection (2018, background): “S UPERVISED object detection has made great progress in recent years [1]–[6], as concluded in the object detection survey [7].”
• Learning an Evolutionary Embedding via Massive Knowledge Distillation (2020, background): “With the development of deep learning [31, 32], various deep neural networks have made great improvements on different computer vision tasks.”
• Pixel Difference Networks for Efficient Edge Detection (2021, methodology): “Then, the bag-of-words technique [27] is usually used to calculate the code distribution (or histogram), which is regarded as the image representation.”
• Deep Structure-Revealed Network for Texture Recognition (2020, background): “medical image analysis, industrial visual inspection, image classification/retrieval [14, 27, 21, 20, 34, 30].”