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As an effective dimensionality reduction method, feature selection can remove the irrelevant variables and increase the accuracy in machine learning. In this paper, a feature selection method based on grouped sorting is proposed to solve the problem of high-dimensional data processing. As in this work, feature grouping is firstly carried out with the redundancy between the features as the group criteria...
Texture analysis is an important research content in pattern recognition and computer vision, and we can get important information from the image through it. As an important method in feature extraction and classification, texture analysis has a very wide range of applications in the field of scientific research and engineering technology. In order to solve the problem of image classification, feature...
This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and...
This paper presents a genetic algorithm (GA) approach for parameters optimization of support vector machine, which is used for the object-oriented classification of high spatial resolution images over urban area. The proposed method is a three-step routine involves the integration of 1) image segmentation, 2) GA-based parameter optimization of Support vector machine (SVM), and 3) objected-based classification...
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