Optimization of CNN-based image retrieval process based on Fourier transform and entropy

In image retrieval, two main indicators are focused: accuracy and efficiency. Focusing on improving the indicators, this article proposes an optimized method for image retrieval based on CNN features based on Fourier transform and entropy. Using Fourier transform to describe images more accurately to improve accuracy, Using entropy to binary describe images to improve efficiency. We evaluated the effectiveness of our method using Vgg16, Resnet18, and ShuffleNetV2 networks on the UKbench, Holidays, and Wang datasets. On UKbench, the average improvement in retrieval accuracy is 0.0254, and the average improvement in retrieval efficiency is 22.12%. On Holidays, the average retrieval accuracy improved by 2.65% and the average retrieval efficiency improved by 18.63%. On Wang (top 20), the average retrieval accuracy improved by 0.23%, and the average retrieval efficiency improved by 37.58%. The experimental results show that the proposed method can effectively improve accuracy and efficiency.