Multiclass image classification method based on active learning and semi-supervised learning

A semi-supervised learning and active learning technology, applied in character and pattern recognition, instruments, calculations, etc., can solve the problems of waste of useful information, failure to describe the uncertainty of sample classification well, and failure to consider information, etc., to achieve rapid provision of , efficient image classification effect, and the effect of taking into account the requirements of computational complexity

Inactive Publication Date: 2010-10-06
WUHAN UNIV
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Problems solved by technology

At present, some experts in this field have discussed the problem of classification uncertainty measurement in multi-class classification active learning. Aiming at the problem that entropy cannot describe the classification uncertainty of samples well in multi-class problems, a method based on the most Active Learning Sample Selection Criteria for Bes...

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  • Multiclass image classification method based on active learning and semi-supervised learning
  • Multiclass image classification method based on active learning and semi-supervised learning
  • Multiclass image classification method based on active learning and semi-supervised learning

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Embodiment Construction

[0029] The core of the multi-class image classification method based on active learning and semi-supervised learning proposed by the present invention mainly includes three parts: active learning based on optimal and suboptimal labels (BvSB), self-training (CST) semi-analysis with constraints Supervised learning, and support vector machine (SVM) classifiers.

[0030] Support vector machine (SVM) classifiers are well known to those skilled in the art.

[0031] The active learning method based on optimal label and suboptimal label (BvSB) is one of the uncertainty sampling methods. In multi-class classification tasks, it can effectively select the samples with the highest classification uncertainty for the current classifier, so that better classification performance can be obtained with fewer training samples. The basic block diagram of BvSB active learning can be found in the appendix figure 1 .

[0032] Self-training (Self-Training) is a technique commonly used in semi-supe...

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Abstract

The invention relates to the technical field of image information processing, in particular to a multiclass image classification method based on active learning and semi-supervised learning. The method comprises five steps: initial sample selection and classifier model training, BvSB active learning sample selection, CST semi-supervised learning, training sample set and classifier model updating and assorting process iteration. Through the operations of BvSB active learning sample selection, CST semi-supervised learning and SVM classification, the invention has efficient image classification effect under the condition of less manual tagging, does not increase overmuch computation burden, can quickly provide classification effects and also can take consideration of the demand of a classification system on the computation complexity.

Description

technical field [0001] The invention relates to the technical field of image information processing, in particular to a multi-category image classification method based on active learning and semi-supervised learning. Background technique [0002] As an important application in image processing, image classification has always been a very active field in image processing. In recent years, many researchers have done a lot of research on image classification, and proposed many classification algorithms, such as Support Vector Machines (Support Vector Machines, SVMs) method, artificial neural network (Artificial Neural Network, ANN), genetic algorithm (Genetic Algorithm, GA), AdaBoost, Random Forest (Random Forest), etc. Most image supervised classification algorithms are based on statistical models. Users need to manually label a large number of image samples, and then obtain the model through training from training samples with category labels. [0003] In practical applica...

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Application Information

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IPC IPC(8): G06K9/66
Inventor 曹永锋陈荣殷慧
Owner WUHAN UNIV
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