Picture multi-level classification method and system based on cascade mean vector comprehensive scoring

Abstract

The invention relates to the field of image classification, and provides a picture multi-level classification method and system based on cascade mean vector comprehensive scoring. The method comprises the steps: constructing a sample image feature vector set through a sample image set, wherein the sample image feature vector set comprises a plurality of feature categories; calculating a mean vector of each feature category in the sample picture feature vector set to obtain a mean vector set; extracting to-be-classified picture feature vectors of the to-be-classified pictures; iteratively calculating the Euclidean distance between the feature vector of the to-be-classified picture and the mean vector of each feature category in the mean vector set to obtain an Euclidean distance set; and performing calculating to obtain a classification result of the to-be-classified picture through the Euclidean distance set. According to the method, the multi-level classification accuracy can be improved, and the problems that the classification accuracy is low due to the fact that inter-class hydrophilicity and hydrophobicity are not considered and the classification accuracy is low due to unbalanced sample distribution are effectively solved; and the problem that the sample warehouse is difficult to update is effectively solved, and the sample warehouse updating cost is greatly reduced.

Description

technical field [0001] The invention relates to the field of image classification, in particular to a method and system for multi-level classification of pictures based on cascaded mean vector comprehensive scoring. Background technique [0002] In the process of grassroots social governance work, grid personnel need to fill in the event types when they report issues such as inspections and visits, basic data collection, information reporting, conflict investigation and mediation, and event types are often manifested as multi-level cascading classifications, and The three-level classification is the most common. There are many types of incidents in grassroots social governance, and the classification is detailed. Taking Nanshan District of Shenzhen as an example, incidents can be divided into more than 20 first-level classifications, more than 50 second-level classifications, and more than 300 third-level classifications. This makes the grid It is difficult for the personne...

AI Technical Summary

Problems solved by technology

[0007] The main purpose of the present invention is to solve the technical problems in the prior art that the classification results are easily confused due to lack of consideration of the closeness between classes, and the unbalanced distribution of samples will affect the accuracy of the classification model

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