Image Retrieval Based On Deep Learning

Paper main researches to image retrieval with deep learning approach.Because for information resources,the image not only carries abundant information,and has more intuitive form.In the era of big data intelligence,how to quickly and accurately find the target in the vast images as the sea,which is the meaning of image retrieval.This paper studies the main contents including:(1)From different angles,this paper researches the current main machine learning algorithms:On the one hand,from the Angle of the input data,supervised learning and unsupervised learning algorithms are analyzed and discussed,and their advantages and disadvantages are studied;On the other hand,from the perspective of the level of the machine learning model,shallow and deep learning algorithms are analyzed and discussed,and their advantages and disadvantages are studied.Finally,the paper also analyzed the commonly used models and algorithms of the depth study.(2)The paper analyzed Deep Belief Network(DBN)based on restricted boltzmann machine(RBM).The paper,based on the current improvement in all sorts of DBN network analysis and research,found that the core algorithm of DBN network implements the depth structure more easily,and the data exception handling is more perfect and easy to expand,so the paper choose study of DBN network based on the RBM.But the DBN network also has some problems,mainly in the DBN network for data dimension reduction without considering the two-dimensional image structure.(3)Aimed at the shortcoming of DBN network based on RBM in the image input processing disadvantage,S-DBN algorithm is proposed,and the main innovation point of the S-DBN algorithm is that by pooling layer to merger similar characteristics Before feature extraction in image data.the improvements to improve classification accuracy,reduces the training time of feature extraction.