Image Retrieval Based on Deep Convolutional Neural Network and Locality Sensitive Hash

Network image resources are growing rapidly. How to achieve fast and effective large-scale image retrieval has become one of the hotspots of current research. In this paper, the typical deep convolutional neural network VGG16 is used to extract the features of the image dataset to be retrieved using the output of the network connection layer on the pre-trained model to establish the index, and the local sensitive hash algorithm is used to improve the retrieval speed and performs an end-to-end content-based image retrieval tasks. The image retrieval method designed in this paper provides an effective method for large-scale image retrieval under limited computing resources.