Multiclass image classification method based on semi-supervised extreme learning machine

Abstract

The invention discloses a multiclass image classification method based on a semi-supervised extreme learning machine. A training sample set is divided into an already marked sample set L, an unmarked sample set U and a test set T; training subsets of N differences are constructed by carrying out replaceable resampling on samples in the U; semi-supervised extreme learning machine models are respectively trained with the already marked training sample L on each subset, wherein all together there are N classifiers; the sum of output of corresponding nodes of N extreme learning machines is solved and an average is taken; after normalization processing on the output, uncertainty evaluation is carryed out on the samples by use of the active learning technology of a best mark and second best mark algorithm BvSB, most uncertain samples are taken from the U for manual marking and are transferred to the L; and a classifier model is re-updated until iteration ends. Through the method provided by the invention, the problems of low classification correction rate and low learning speed existing in image classification in related arts are solved, and a certain foundation is laid for accurate, rapid and stable image classification.

Description

technical field [0001] The invention relates to pattern recognition and machine learning technology, in particular to a multi-category image classification method integrating active learning and semi-supervised extreme learning machines. Background technique [0002] In recent years, with the rapid development of multimedia technology and Internet communication, the problem of image classification has attracted the attention of many researchers, and various image classification algorithms have emerged in an endless stream. However, many traditional image classification algorithms are researched based on supervised learning, which requires a large number of labeled samples before training to establish an accurate classifier model and achieve correct classification. While this repetitive labeling work is time-consuming and expensive, it is easy to collect large numbers of unlabeled samples. For example, in computer-aided medical image analysis, a large number of medical image...

AI Technical Summary

Problems solved by technology

[0009] Purpose of the invention: In order to overcome the problems of low image classification accuracy and low learning speed in the prior art, the present invention provides a multi-class image classification method based on semi-supervised extreme learning machine, which adopts unlabeled learning technology to integrate semi-supervised The active learning algorithm of the extreme learning machine SS-ELM and the optimal labeling and suboptimal labeling algorithm BvSB uses the method of multi-classifier fusion and selection of the voting committee QBC to classify multi-category images; compared with traditional supervised learning algorithms, The algorithm can deal with multi-class image classification problems more accurately, quickly and stably

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