mCLOUD: A Multiview Visual Feature Extraction Mechanism for Ground-Based Cloud Image Categorization

Author:

Xiao Yang1,Cao Zhiguo1,Zhuo Wen2,Ye Liang1,Zhu Lei1

Affiliation:

1. National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, China

2. School of Electrical Engineering, Southwest Jiaotong University, Emeishan, China

Abstract

AbstractIn this paper, a novel Multiview CLOUD (mCLOUD) visual feature extraction mechanism is proposed for the task of categorizing clouds based on ground-based images. To completely characterize the different types of clouds, mCLOUD first extracts the raw visual descriptors from the views of texture, structure, and color simultaneously, in a densely sampled way—specifically, the scale invariant feature transform (SIFT), the census transform histogram (CENTRIST), and the statistical color features are extracted, respectively. To obtain a more descriptive cloud representation, the feature encoding of the raw descriptors is realized by using the Fisher vector. This is followed by the feature aggregation procedure. A linear support vector machine (SVM) is employed as the classifier to yield the final cloud image categorization result. The experiments on a challenging cloud dataset termed the six-class Huazhong University of Science and Technology (HUST) cloud demonstrate that mCLOUD consistently outperforms the state-of-the-art cloud classification approaches by large margins (at least 6.9%) under all the different experimental settings. It has also been verified that, compared to the single view, the multiview cloud representation generally enhances the performance.

Publisher

American Meteorological Society

Subject

Atmospheric Science,Ocean Engineering

Reference34 articles.

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