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The high-dimensional feature vectors of hyper spectral data often impose a high computational cost as well as the risk of "over fitting" when classification is performed. Therefore it is necessary to ...
The use of satellite imagery in the derivation of land cover information has yielded immense dividends to numerous application fields such as environmental monitoring and modeling, map making and rev...
For the purposes of our study, an ASTER satellite image was used, acquired on 5 October, 2001, with 14 spectral bands resampled at the spatial resolution of 15 m. The study area concerns the geograph...
Hyper-spectral remote sensing increases the volume of information available for research and practice, but brings with it the need for efficient statistical methods in sample spaces of many dimensio...
We study filter–based feature selection methods for classification of biomedical images. For feature selection, we use two filtersa relevance filter which measures usefulness of individual features...
The development of lidar system, especially incorporated with high-resolution camera components, has shown great potential for urban classification. However, how to automatically select the best featu...
Mapping of Landscape Protection Areas with regard to user requirements for detailed land cover and biotope classes has been limited by the spatial and temporal resolution of Earth observation data. ...
Automatic facial expression recognition for novel individuals from 3D face data is a challenging task in pattern analysis. This paper describes a feature selection process for pose-invariant 3D facial...
Maximizing the joint dependency with a minimum size of variables is generally the main task of feature selection. For obtaining a minimal subset, while trying to maximize the joint dependency with the...
The availability of numerous spectral, spatial, and contextual features renders the selection of optimal features a time consuming and subjective process in object-based image analysis (OBIA). While ...
为提高基于机器视觉的棉花异性纤维在线分类的精度和速度,提出了一种基于改进遗传算法的特征选择方法。采用分段式染色体管理方案实现对多质特征空间局部化管理;利用分段交叉和变异算子避免出现无效染色体,提高搜索效率;通过自适应调整交叉和变异概率实现强搜索能力和快收敛速度的动态平衡。实验结果表明,该方法比基本遗传算法搜索能力更强、收敛速度更快,所得最优特征子集较小,更适用于棉花异性纤维在线分类。 An opt...
Various multi-echo and Full-waveform (FW) lidar features can be processed. In this paper, multiple classifers are applied to lidar feature selection for urban scene classification. Random fores...
This paper considers the semantic gap in content-based image retrieval from two aspects: (1) irrelevant visual contents (e.g. background) scatter the mapping from image to human perception; (2) unsup...
This paper considers the semantic gap in content-based image retrieval from two aspects: (1) irrelevant visual contents (e.g. background) scatter the mapping from image to human perception; (2) unsupe...
When the appearances of the tracked object and surrounding background change during tracking, fixed feature space tends to cause tracking failure. To address this problem, we propose a method to embed...

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