Access the full text.
Sign up today, get DeepDyve free for 14 days.
Jianrong Zhang, Hongwei Zhao, Jiao Li (2021)
TRS: Transformers for Remote Sensing Scene ClassificationRemote. Sens., 13
Yang Liu, Qingchao Chen, Wei Chen, I. Wassell (2018)
Dictionary Learning Inspired Deep Network for Scene Recognition
Genevieve Patterson, Chen Xu, Hang Su, James Hays (2014)
The SUN Attribute Database: Beyond Categories for Deeper Scene UnderstandingInternational Journal of Computer Vision, 108
Alejandro López-Cifuentes, Marcos Escudero-Viñolo, Jesús Bescós, Álvaro García-Martín (2019)
Semantic-Aware Scene RecognitionArXiv, abs/1909.02410
G Chen, X Song, H Zeng (2020)
Scene recognition with prototype-agnostic scene layoutIEEE Trans Image Process, 29
H Zeng, X Song, G Chen (2022)
Amorphous region context modeling for scene recognitionIEEE Trans Multimed, 24
P Lv, W Wu, Y Zhong (2022)
SCViT: a spatial-channel feature preserving vision transformer for remote sensing image scene classificationIEEE Trans Geosci Remote Sens, 60
Siyuan Hao, Bin Wu, Kun Zhao, Y. Ye, Wei Wang (2022)
Two-Stream Swin Transformer with Differentiable Sobel Operator for Remote Sensing Image ClassificationRemote. Sens., 14
G Cheng, Z Li, X Yao (2017)
Remote sensing image scene classification using bag of convolutional featuresIEEE Geosci Remote Sens Lett, 14
Boheng Chen, Jie Li, G. Wei, Biyun Ma (2018)
A novel localized and second order feature coding network for image recognitionPattern Recognit., 76
A Vaswani (2017)
Attention is all you needAdv Neural Inf Process Syst, 30
B Zhou, A Lapedriza, A Khosla (2017)
Places: a 10 million image database for scene recognitionIEEE Trans Pattern Anal Mach Intell, 40
Z Wang, L Wang, Y Wang (2017)
Weakly supervised patchnets: describing and aggregating local patches for scene recognitionIEEE Trans Image Process, 26
H Zeng, X Song, G Chen (2019)
Learning scene attribute for scene recognitionIEEE Trans Multimed, 22
E Li, J Xia, P Du (2017)
Integrating multilayer features of convolutional neural networks for remote sensing scene classificationIEEE Trans Geosci Remote Sens, 55
Lin Xie, Feifei Lee, Li Liu, K. Kotani, Qiu Chen (2020)
Scene recognition: A comprehensive surveyPattern Recognit., 102
L Liu, P Wang, C Shen (2017)
Compositional model based fisher vector coding for image classificationIEEE Trans Pattern Anal Mach Intell, 39
Scene classification based on convolutional neural networks (CNNs) has achieved great success in recent years. In CNNs, the convolution operation performs well in extracting local features, but its ability to capture global feature representations is limited. In vision transformer (ViT), the self-attention mechanism can capture long-term feature dependencies, but it breaks down the details of local features. In this work, we make full use of the advantages of the CNN and ViT and propose a Transformer-based framework that combines CNN to improve the discriminative ability of features for scene classification. Specifically, we take the deep convolutional feature as the input and establish the scene Transformer module to extract the global feature in the scene image. An end-to-end scene classification framework called the FCT is built by fusing the CNN and scene Transformer module. Experimental results show that our FCT achieves a new state-of-the-art performance on two standard benchmarks MIT Indoor 67 and SUN 397, with the accuracy of 90.75% and 77.50%, respectively.
International Journal of Multimedia Information Retrieval – Springer Journals
Published: Dec 1, 2022
Keywords: Scene classification; Convolutional neural networks; Vision transformer; Deep learning
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.