Research on image retrieval based on the convolutional neural network

The development of the Internet has led to the accumulation of a large number of images in various databases. People are eager to find useful information in these databases which stimulate the development of image retrieval technologies. In this paper, we mainly study image retrieval based on the convolutional neural network. The study is divided into four parts to explore characteristics of convolution neural networks used in image retrieval. The first part introduces the structure of the convolutional neural network and the method of extracting features from images. The second part compares the effects of different similarity measures on retrieval accuracy. The third part studies the way to speed up retrieval. We use PCA to reduce feature dimensions and draw a line chart of dimension and accuracy. Then we analyze the reason why the change of accuracy rate is divided into two stages: ascending first and descending later. The fourth part studies the way to increase retrieval accuracy. We compare the retrieval accuracy before and after fine-tuning and analyze the reasons for that. In the end, we sum up the whole text and summarize key points that we should consider when designing an image retrieval system based on the convolutional neural network.