Improved method based on extreme learning machine (ELM) and sparse representation classification

Abstract

The invention discloses an improved method based on an extreme learning machine (ELM) and sparse representation classification. The method comprises the following steps of 1, randomly generating a hidden layer node parameter; 2, calculating a hidden layer node output matrix; 3, according to a size relation of L and N, using different formulas to calculate an output weight connecting a hidden layer node and an output neuron; 4, calculating an output vector of a query picture y; 5, determining a difference value of a maximum value of and a secondary maximum value os in an ELM output vector o, and if the difference value is greater than a set value, determining an index corresponding to the maximum value in the output vector, wherein the index is a type which the query picture belongs to; otherwise, entering into step6; step6, using a training sample corresponding to the k maximum values in the output vector o to construct a dictionary, using a coefficient reconstruction algorithm to calculate a linear representation coefficient of the picture y, calculating a residual error and determining the type which the query picture belongs to according to the type corresponding to the residual error. In the invention, a calculated amount is greatly reduced, a high recognition rate is realized and calculating complexity can be greatly reduced.

Description

technical field [0001] The invention belongs to the field of image classification, in particular to an improved classification method based on extreme learning machine and sparse representation. Background technique [0002] Image classification, that is, automatically grouping input images into a specific category, has attracted more and more attention, especially because of its applications in security systems, medical diagnosis, human-computer interaction and other fields. In the past few years, some techniques developed from machine learning have also had a great impact in the field of image classification. In fact, almost every approach proposed in the past has its advantages and disadvantages. An unavoidable problem is the trade-off between computational complexity and classification accuracy. In other words, it is impossible to design a method that has the best efficiency and recognition rate in all applications. In order to solve this problem, a hybrid system emer...

AI Technical Summary

Problems solved by technology

Experiments show that ELM-SRC performs better than the extreme learning machine (ELM) in terms of recognition rate, and the computational complexity is lower than that of the sparse representation classification (SRC). However, due to the use of a complete dictionary, the ELM and sparse representation classification (ELM-SRC) Computational complexity is still high

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