Image similarity learning method for extracting multi-resolution features of image

Abstract

The invention discloses an image similarity learning method for extracting image multi-resolution features. The method comprises the following steps: 1, acquiring a chip slot image by adopting a laboratory industrial camera, performing normalization processing on images, combining every two similar or dissimilar single images into a dual-channel image according to similarity and dissimilarity ofthe images through human visual judgment and a hash algorithm to form an input image pair data set, and dividing the input image pair data set into a training set and a test set; 2, constructing a network model, selecting a deep learning framework, specifying a network training target function and an optimizer, and performing similarity learning; 3, performing network model training and testing. The problems that in existing image similarity learning, image feature information extracted through a network is single, image features cannot be accurately described, network training is limited by the data size, and overfitting is likely to happen are solved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image similarity learning method for extracting image multi-resolution features. Background technique [0002] Image similarity learning is to characterize the correlation between images by mining image content information. In the realization of technologies such as face recognition, image camouflage evaluation, image retrieval, image quality evaluation, and pedestrian recognition, image feature information can be accurately and quickly described by learning image similarity. In the study of image similarity learning, traditional machine learning methods mainly use the calculation of image feature vector cosine distance and Euclidean distance, mining image structure similarity (SSIM), and artificially setting feature descriptors to represent the similarity between images. , The extracted image feature information is less, resulting in insufficient similar...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide an image similarity learning method for extracting multi-resolution features of images, which solves the problem of the simplification of the image feature information extracted by the network in the existing image similarity learning, which cannot accurately describe the image features and the network training is limited. The amount of data is prone to overfitting problems

Images