Set opening trace image classification method and system

Abstract

The invention relates to a set opening trace image classification method and system. Trace images are automatically classified. The method judges whether to-be-classified images belong to some type in a trace image library or not or a newly increased type in a multi-layer cascaded manner. The method and system provided by the invention have the characteristics of being fast in speed, less in artificial participation and relatively accurate and comprehensive in result, and can be used for automatically expanding the image library and serially and parallelly connecting cases by classifying traces on the scene, thereby providing a great help for police investigation. Therefore, the method and system provided by the invention can be widely used for the field of trace classification.

Description

technical field [0001] The invention relates to a classification method and system, in particular to an open-set trace image classification method and system. Background technique [0002] On-site traces are one of the important physical evidence in criminal investigation, and most of the extracted traces are stored in the form of images. How to efficiently classify and manage on-site trace images has become an urgent problem to be solved. [0003] Trace image classification is a kind of image classification. At present, image classification methods are mainly used to: (1) determine whether the image contains objects of interest, such as people, airplanes, etc.; (2) determine the scene represented by the image, such as forest, Living room and factory, etc.; (3) Determine the morphological attributes of objects in the image, such as color, shape, etc. The basic framework of the image classification method is: (1) need to manually or automatically mark each image in the image...

AI Technical Summary

Problems solved by technology

[0004] The problems existing in the current image classification methods are: (1) The accuracy of the classifier depends on the accuracy of the markers. Since there are many trace images and their quality is poor, and most of the markers of the trace images are automatically marked, it is very normal for the markers to be wrong. It greatly affects the classification accuracy

(2) At present, most image classification methods are based on the similarity between the image to be classified and the common features of various types of images, and the trace images are mostly disturbed and incomplete images. If the common features within the class are obtained, it is possible The useful information of various images is weakened or lost

(3) The current image classification method needs to be retrained if an image is added to the image library. Since there are many images in the trace image library, it is difficult to implement if each new image is added.

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