A target classification method of logistic regression based on principal component analysis

Abstract

The invention relates to a target classification method for logistic regression based on principal component analysis, and belongs to the field of picture processing and pattern recognition. The method is characterized by comprising the following steps: (1) determining color moment characteristics; (2) determining texture feature parameters; (3) determining a characteristic parameter correlation coefficient matrix; (4) determining an input feature vector; and (5) taking the feature vectors obtained in the previous step as input of a logistic regression model, and establishing the logistic regression model for training to realize classification of different training samples. The feature vectors of the sample set are subjected to dimensionality reduction by using a principal component analysis method; the classification method is used as an input feature vector of logistic regression to carry out model training, a complex algorithm process is not needed to realize target classification,the classification method can improve the stability and high efficiency of the model on the basis of ensuring the classification accuracy, and an accurate and stable classification method is provided.

Description

technical field [0001] The invention relates to the field of picture processing and pattern recognition, and mainly relates to a method for classifying picture objects. Background technique [0002] At present, for the classification of image objects, most of the technologies are not robust, unstable, not adaptable to different types of objects, and the classification speed is slow. Although some technologies can achieve high stability, they need to go through a series of complex algorithm processes and run slowly. Taking the classification of fruits and vegetables as an example, when classifying them, some technologies fuse the features of color and texture, classify them through the minimum distance classifier based on the statistics of wavelet transform subbands and co-occurrence features, or use the eight-neighbor The domain analysis method extracts and marks the edge of the connected area of ​​the target, and compares the color feature parameters and shape feature para...

AI Technical Summary

Problems solved by technology

[0003] At present, the target classification technology in pictures is often related to economy and business, and the technology put into use must ensure high robustness and high efficiency, because if the technology is unstable, it may lead to an increase in the error rate of target classification

The classification speed is not fast enough, which may reduce the efficiency of target classification

This may cause a series of business problems and even bring financial losses

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