Weighted hyper-sphere support vector machine algorithm based image classification method

Abstract

The invention discloses a weighted hyper-sphere support vector machine algorithm based image classification method which can improve the calculation speed and the calculation accuracy. The weighted hyper-sphere support vector machine algorithm based image classification method comprises collecting image data; denoising and performing normalization processing on images, calculating characteristics of HOG (Histograms of Oriented Gradients) of the images, adding predefined categories into image categories and classifying the images; calculating a data center of every category, calculating the weight of every sample of the category of data according to the data center and ordering the training samples according to the weight; training a hyper-sphere support vector machine through a multithreading genetic algorithm and an SMO (Sequential Minimal Optimization) method and solving an optimal parameter and a corresponding hyper-sphere model; extracting HOG characteristics of the newly collected images, calculating position relationships between newly collected samples and the hyper-sphere model, obtaining label categories according to a distinguish rule and labelling categories of the newly collected images.

Description

technical field [0001] The invention relates to the image multi-classification problem in the field of pattern recognition, in particular to an image classification method based on a weighted hypersphere support vector machine algorithm that can improve calculation speed and classification accuracy. Background technique [0002] Image multi-category classification technology is currently widely used in research fields such as video surveillance target recognition, large-scale image query, and image semantic understanding. The implementation process is divided into three steps: first, image preprocessing, extracting image features; second, using different trainers for training according to the features; third, using the training model to classify images. With the continuous development of machine learning theory, many latest classifier models, such as kNN, clustering, neural network, etc., have been widely used in the field of image multi-category classification. As a new ma...

AI Technical Summary

Problems solved by technology

In order to reduce the complexity of solving the quadratic programming, some researchers proposed the hypersphere support vector machine algorithm that directly calculates the hyperspheres of each category, and converts the quadratic programming problem of the binary classification hyperplane into the quadratic programming of the hypersphere. The basic idea of ​​solving the problem is to calculate the minimum enclosing sphere of each category of training data separately. It is required that the radius of the minimum enclosing sphere be as small as possible, and try to contain all the sample points of this category, but its calculation speed and classification accuracy need to be further improved

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