Classification method aiming at small sample and high dimensional images

Abstract

The invention discloses a classification method aiming at small sample and high dimensional images. The classification method aiming at the small sample and high dimensional images comprises the following steps: (1) gaining a first classification rule, (2) classifying images on a first level, (3) gaining a second classification rule, (4) classifying the images on a second level, (5) gaining a third classification rule, (6) classifying the images on a third level and gaining a classification result. The classification method aiming at the small sample and high dimensional images is combined with characteristics of industrial manufacture. The first-level image classification has strong manual controllability, and meanwhile combines a manifold dimensionality reduction method and superiorities of a support vector machine, thereby being suitable for the classification of the small sample and high dimensional images. Through combining a direct expression method of image type, the manifold dimensionality reduction method, and a support vector machine classification method with an arborescence topological structure classification method based on position features and barycenter features, a three-level image classification method is established. Due to the fact that the data transmission quantity between the image classifiers of the three levels is small, efficiency can not be affected. The classification method aiming at the small sample and high dimensional images is simple in operation, good in algorithm connection and few in input parameters.

Description

technical field [0001] The invention relates to an image classification method, in particular to a classification method for small samples and high-dimensional images. Background technique [0002] The image classification method is an image processing method that distinguishes different types of objects according to the different characteristics reflected in the image information. It uses computer to quantitatively analyze the image, and classifies the image or each pixel or area in the image into one of several categories to replace human visual interpretation. After years of development, image classification methods have been applied in various fields, such as geographic information classification, steel surface defect recognition, fabric surface defect recognition, wood surface defect recognition, agricultural product classification and recognition, etc., but in the field of industrial image classification, such as However, the existing methods have the following defect...

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Problems solved by technology

After years of development, image classification methods have been applied to various fields, such as geographic information classification, steel surface defect recognition, fabric surface defect recognition, wood surface defect recognition, agricultural product classification and recognition, etc., but in the field of industrial image classification, such as However, the existing methods have the following defects in the image recognition of strip surface defects: (1) the classification effect for high-dimensional images is not ideal; (2) the classification rate is not high when the training samples are small compared to the images to be classified; (3) The input parameters are complex, different parameters seriously affect the recognition effect, and cause difficulties in debugging and maintenance

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