A multi-instance and multi-label scene classification method based on multi-kernel fusion

Abstract

The present invention relates to a multi-instance multi-label scene classification method based on multi-core fusion, comprising: inputting a multi-instance multi-label data set, splitting it into a multi-instance data set and a multi-label data set; Establish a correlation matrix for each packet in the data set; calculate the basic kernel function between every two multi-instance data packets under the same threshold according to the obtained correlation matrix, and the basic kernel function values ​​form the basic kernel matrix; combine the basic kernel functions under different thresholds The element values ​​at the same position in the kernel matrix are convexly combined to obtain a multi-kernel matrix; using multi-label data set training, multiple multi-kernel SVM classifiers are obtained. The multi-core SVM classifier is used to predict the label set of unknown multi-instance data packets to achieve scene classification. A multi-instance and multi-label scene classification method based on multi-core fusion of the present invention improves scene classification accuracy. The invention also relates to a multi-instance multi-label scene classification system based on multi-core fusion.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a multi-instance multi-label scene classification method based on multi-core fusion. Background technique [0002] Multi-instance learning is a learning method evolved from supervised learning. It was first proposed in the 1990s when people were studying drug activity. It regards each pharmaceutical molecule as a package, and each isomer of the molecule is viewed as As an example in the package, if the molecule has an isomer that is suitable for pharmaceuticals, then the package corresponding to the molecule is marked as a positive package, otherwise it is marked as a negative package. Through this method, a learning system is finally constructed, and then the Molecules that are known to be suitable or unsuitable for pharmaceuticals are learned to correctly predict whether other new molecules are suitable for pharmaceuticals. Since then, multi-instance learning has been...

AI Technical Summary

Problems solved by technology

[0004] Although the above algorithms have achieved good results in solving the multi-instance multi-label problem, they do not consider the correlation of examples in the bag.

In many practical applications, especially in scene classification problems, the independence assumption of examples is difficult to guarantee, which will lead to unsatisfactory classification results.

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