一种使用边缘增强技术提高相似图片检索召回率的方法

针对大规模图像分类处理中图像旋转或背景变换导致的配准度较低问题,提出一种基于边缘增强的卷积神经网络图像分类方法。该方法通过VGG19网络模型提取图像特征,并使用余弦相似度进行图像分类判定,利用边缘增强突出图像主体的边缘特征,降低图像旋转或背景变换对VGG19网络分类性能带来的影响。实验证明,该方法可以有效地提高同一主体旋转图像和背景变换图像与原始图像的相似度,适用于各类图像的分类。

A method for improving recall rate of similar image retrieval by using edge enhancement technology

Aiming at the problem of low registration caused by image rotation or background transformation in large-scale image classification processing,a convolutional neural network image classification method based on edge enhancement was proposed.This method extracted image features through the VGG19 network model,and used cosine similarity for image classification judgment,used edge enhancement to highlight the edge features of image subject,and reduced the impact of image rotation or background transformation on the classification performance of the VGG19 network.Experiments prove that this method can effectively improve the similarity between the same subject rotated image and background transformed image and the original image,and is suitable for the classification of various images.