Bag-of-Visual-Words Scene Classifier With Local and Global Features for High Spatial Resolution Remote Sensing Imagery

By Qiqi Zhu, Yanfei Zhong, Bei Zhao, Gui-Song Xia, Liangpei Zhang. 2016

328 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology

How researchers see this paper

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

In Atmospheric Science

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks (2019, neutral): “Traditional approaches are mostly based on the Bag-of-Visual-Words model (Sivic and Zisserman, 2003; Zhu et al., 2016), but their potential for improvement was limited by the ability of experts to design the feature extractor and the expressive power encoded.”
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, methodology): “Thanks to the simplicity and efﬁciency of these feature encoding methods, they have been broadly applied to the ﬁeld of aerial image scene classiﬁcation [47]–[52], whereas the representation capability of handcrafted features is limited.”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “Usually they are used to construct bag-of-visualwords (BoVW) models [30], [31], [34], [62], [66]–[71] and HOG”
Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective (2018, positive): “The bag-of-keypoints/bag-of-visual-wordsmethod,ananalogytothebag-of-wordsmethod for text categorization using keywords [41–44], has also been used as a feature representation tool in image analysis in which the SIFT algorithm is used for keypoint detection and local feature description [45, 46].”
Building Instance Classification Using Street View Images (2018, background): “Handcrafting features have been well studied for decades, such as scale-invariant feature transform (SIFT) (Lowe, 1999) encoded by bag of visual words (BoVW) (Yang and Newsam, 2010; Cheriyadat, 2014; Zhu et al., 2016), multiple textural features (Xu et al.”
Deep learning in remote sensing: a review (2017, methodology): “Traditional approaches are mostly based on the Bag-of-Visual-Words (BoVW) model [78, 79], but their potential for improvement was limited by the ability of experts to design the feature extractor and the expressive power encoded.”