Abstract:Deep learning is a new filed in machine learning research, and to apply it to computer vision achieves effective result. To solve the problem that the traditional Scale-Invariant Feature Transform algorithm (SIFT) has low efficiency and extracts image features roughly, A SIFT image retrieval algorithm based on deep learning is proposed. The algorithm idea is that on the Spark platform, a deep Convolutional Neural Network (CNN) model is used for SIFT feature extraction, and Support Vector Machine (SVM) is utilized for unsupervised clustering of image library, then the adaptive image feature measures are used to re-sort the search results to improve the user experience. The experiment results on the Corel image set show that compared with the traditional SIFT algorithm, the precision and recall rate of the SIFT image retrieval algorithm based on deep learning is increased by about 30 percentage points and the retrieval efficiency is improved, the resulting image order is also optimized.