An open-set trace image classification method and system

Abstract

The invention relates to an open-collection trace image classification method and system, which realize automatic classification of trace images. It uses a multi-layer cascading method to judge whether the image to be classified belongs to a certain category or a new category in the trace image library. The invention has the characteristics of fast speed, less manual participation, and relatively accurate and comprehensive results, and can automatically expand the image library and realize the serialization of cases through the classification of on-site traces, which provides great help for the police to handle cases. Therefore, the present invention can be widely used in 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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