Building robust neighborhoods for manifold learning-based image classification and anomaly detection

T. Doster, C. Olson
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引用次数: 8

Abstract

We exploit manifold learning algorithms to perform image classification and anomaly detection in complex scenes involving hyperspectral land cover and broadband IR maritime data. The results of standard manifold learning techniques are improved by including spatial information. This is accomplished by creating super-pixels which are robust to affine transformations inherent in natural scenes. We utilize techniques from harmonic analysis and image processing, namely, rotation, skew, flip, and shift operators to develop a more representational graph structure which defines the data-dependent manifold.
基于流形学习的图像分类和异常检测的鲁棒邻域构建
我们利用流形学习算法在涉及高光谱陆地覆盖和宽带红外海事数据的复杂场景中执行图像分类和异常检测。标准流形学习技术的结果通过包含空间信息而得到改善。这是通过创建对自然场景中固有的仿射变换具有鲁棒性的超像素来实现的。我们利用谐波分析和图像处理技术,即旋转,倾斜,翻转和移位算子来开发更具代表性的图结构,该图结构定义了依赖数据的流形。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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