Similarity Image Retrieval Model based on Local Feature Fusion and Deep Metric Learning

Abstract

With the development of deep learning, this technology has unique advantages in feature extraction and feature nonlinear mapping, and it has become the primary solution to computer vision problems. Research on similarity image retrieval has turned machine learning into deep learning. Generally, similarity image retrieval systems based on deep learning first train a model for image classification, and then extract bottleneck layer features as image representation features for storage and retrieval. The problems with such a design are: the bottleneck layer features lose image detail information, and the features of similar images are not compact enough. This article aims to improve the method for the above problems. Adding metric learning to constrain the image features to solve the problem of inadequate compact image features; using local feature fusion features instead of bottleneck layer features as image representation features can express more image content. Finally, the effectiveness of the improvement proposed in this paper is verified by experiments.