多专家多光谱图像二值化框架
A Multiple-Expert Binarization Framework for Multispectral Images
摘要：
在这项工作中，提出了一个多专家的多光谱图像二值化框架。该框架是基于一个受约束的子空间选择限制的光谱带结合国家的最先进的灰度二值化方法。该框架使用二值化包装器来增强灰度二值化的性能。非线性预处理的各个光谱带被用来增强文本信息。进化优化被认为是获得最佳的和一些次优的3波段子空间，然后形成一个合奏的专家。该框架应用于地面实况多光谱数据集，取得了良好的效果。此外，开发了交叉验证方法的泛化，不仅评估了框架的泛化能力，还提供了所选专家的实际实例，尽管给定的地面实况数据集很小，但这些专家可以应用于看不见的输入。
英文摘要：
In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.
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