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Image classification using wavelet features

机译：使用小波特征进行图像分类

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Abstract: Frequently, real world data is degraded by under sampling of intrinsic periodicities, or by sampling with unevenly spaced intervals. This results in dropout or missing data, and such data sets are particularly difficult to process using conventional image processing methods. In many cases, one must still extract as much information as possible from a given data set, although available data may be sparse or noisy. In such cases, we suggest algorithms based on wavelet transform will offer a viable alternative as some early work in the area has indicated. An architecture of an image classification software system is suggested to implement an improved scheme for the analysis, representation, processing and classification of images. The scheme is based on considering the segments of images as wavelets so that small details in the images can be exploited. The objective is to implement this scheme automatically and rapidly decompose a 2D image into a combination of elemental images so that an array of processing methods can be applied. Thus, the scheme offers potential utility for analysis of images and compression of image data. Moreover, the elemental images may be considered patterns that the system is required to recognize, so that the scheme offers potential utility for industrial and military applications involving robot vision and/or automatic recognition of targets. !28
机译：摘要：通常，通过固有周期的欠采样或以不均匀间隔的采样对真实世界的数据进行降级。这导致数据丢失或丢失，并且使用常规图像处理方法特别难以处理这种数据集。在许多情况下，尽管可用数据可能稀疏或嘈杂，但仍然必须从给定的数据集中提取尽可能多的信息。在这种情况下，我们建议基于小波变换的算法将提供可行的替代方案，如该地区的一些早期工作所表明的那样。提出了一种图像分类软件系统的体系结构，以实现用于图像的分析，表示，处理和分类的改进方案。该方案基于将图像的片段视为小波，从而可以利用图像中的小细节。目的是实现该方案，以自动，快速地将2D图像分解为基本图像的组合，以便可以应用一系列处理方法。因此，该方案为图像分析和图像数据压缩提供了潜在的实用性。此外，基本图像可以被认为是系统需要识别的模式，因此该方案为涉及机器人视觉和/或目标自动识别的工业和军事应用提供了潜在的实用性。 ！28

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