Visible component classification method based on SVM

Abstract

The invention discloses a visible component classification method based on SVM. The method comprises the following steps of in a SVM training phase, A, acquiring a sample picture containing several kinds of visible components and taking the sample picture as a training sample; B, segmenting the sample picture into several visible component images; C, extracting an image characteristic of the visible component images, classifying the visible component images based on the image characteristic and constructing several cascaded visible component picture databases; D, constructing several cascaded SVM classifiers and using the corresponding visible component picture database to train; in a visible component identification classification phase, E, extracting the image characteristic of the visible component images to be identified and classified; F, based on the image characteristic, assigning the visible component images to the corresponding SVM classifiers to carry out identification and classification. The plurality of cascaded SVM classifiers are used to carry out multilevel classification on a visible component graph so that classification accuracy of the SVM classifiers to the visible components is effectively increased.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a method for classifying formed components based on SVM. Background technique [0002] Image recognition technology is a relatively popular technology in recent years. Whether it is license plate recognition, face recognition, fingerprint recognition or cell recognition, the object of recognition is relatively single, and in most cases, recognition and classification are realized based on neural networks. The neural network simulates the human nerves and is a simplified description of the cognitive behavior of the human brain. Without a complete theoretical system, it is easy to fall into a local minimum. [0003] SVM, or support vector machine, can get much better results than other algorithms on a small sample training set, and is currently one of the most commonly used and best classifiers. It has a complete theoretical system, is based on the theory of structural ...

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Problems solved by technology

[0005] In view of the deficiencies in the above-mentioned prior art, the object of the present invention is to provide a method for classifying formed components based on SVM, aiming at solving the incompleteness and accuracy of the existing SVM recognition and classification algorithm in the classification and identification of formed components. limited rate problem

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