Land-Use Scene Classification Using a Concentric Circle-Structured Multiscale Bag-of-Visual-Words Model

By Lijun Zhao, Ping Tang, L. Huo. 2014

163 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery (2016, methodology): “To improve the performance of the classification methods based on the BOVW model, a number of strategies for the spatial organization of the midlevel features, such as the concentric-circle-structured strategy [8] and the pyramid of spatial relations strategy [6], have been employed to enhance the a”
Feature significance-based multibag-of-visual-words model for remote sensing image scene classification (2016, methodology): “One is just to concatenate the vectors of different types of low-level features and to construct BOVW based on the concatenated low-level feature vectors as described by Zhao et al.12 Another is to directly concatenate each type of the BOVW feature vector generated from every single type of low-leve”
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery (2015, methodology): “[11] proposed a concentric circle-structured multi-scale BOW (CCM-BOW) model to achieve rotation-invariance; and Cheng et al.”
An Informative Feature Selection Method Based on Sparse PCA for VHR Scene Classification (2016, background): “In contrast to [2] and [3], Vaduva et al.”
Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors (2016, methodology): “2 Concentric circle-structured multiscale BOVW [5] 86.”
Using convolutional features and a sparse autoencoder for land-use scene classification (2016, methodology): “Zhao, Tang, and Huo (2014) presented a concentric circle-based spatial-rotation-invariant representation strategy for describing the spatial information of visual words and proposed a concentric circle-structured multi-scale BOVW method using multiple features.”
Land-Use Classification With Compressive Sensing Multifeature Fusion (2015, methodology): “They all opted for a similar fivefold-based evaluation, except for the work in [4], where a twofold validation was adopted.”
The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification (2016, methodology): “To consider the spatial arrangement of the visual words in the images, different scene classification methods have been proposed with different spatial organization methods, such as the spatial pyramid matching (SPM) method [32,33], the pyramid of spatial relations method [15], and the concentric ci”
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, methodology): “Unlike the conventional co-occurrence approaches [8], [24] that describe”
Continual Learning Approach for Remote Sensing Scene Classification (2020, background): “Among these solutions, one can ﬁnd those based on sparse coding [1], mid-level visual element-oriented features [2], and bag-of-visual-words [3].”
Hierarchical Coding Vectors for Scene Level Land-Use Classification (2016, background): “By far, the Bag of Visual Words (BOVW) [1,2] framework and its variants [3,4] based on spatial relations have become promising remote sensing image representations for land-use classiﬁcation.”
Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning (2016, background): “To overcome this weakness, several spatial variations [9], [10], [30], [32], [33] have been proposed to incorporate the spatial information and demonstrated significant performance improvements.”