Content-Based Image Retrieval System Via Deep Learning Method

Abstract

Faced with the huge image data in the context of big data era, how to effectively manage, describe, and retrieve them has become a hotspot issue in academic circles. In this paper, we propose an end-to-end image retrieval system based on deep convolutional neural network and differential learning method. We first build an image matching dataset based on the gravitational field model, that is to add a similarity score label for each image in the dataset production stage. Then we train the improved deep learning model and verify the effectiveness of the algorithm on three common image matching dataset (i.e., Caltech-101, Holidays and Oxford Paris). Finally, the experimental results show that our improved deep learning model with differential learning method that used for image retrieval system has state-of-the-art image matching performance. The overall retrieval accuracy in Caltech-101, Holidays and Oxford Paris datasets are 88.5%, 94.1 % and 96.2%, respectively. As the number of returned images increases, the image retrieval accuracy of the system decreases slightly and eventually becomes stable at a high value. And the differential learning based retrieval method is superior to many traditional algorithms in terms of image matching accuracy and single image processing speed.