Multi-scale Atrous convolution and depth hash model

Abstract

Abstract How to propose an image retrieval algorithm with adaptable model and wide range of applications for large-scale datasets has become a critical technical problem in current image retrieval. This paper proposes an incremental image retrieval algorithm based on Atrous convolution and deep hashing with an image retrieval system as the research object. The algorithm contains two important parts: the hash function learning part and the incremental hash code mapping part. Firstly, a module is designed called feature-aware to obtain multi-scale global context-aware information. It also keeps the scale and shape of the final extracted deep features invariant. Then, a new incremental hash loss function is designed to maintain the similarity between the query image and the dataset image. The experimental results show that the algorithm model can perform well in incremental image retrieval. It is shown that the algorithm can solve the current problem of low retrieval efficiency and high cost due to retraining models caused by the dramatic increase in the number of images in the image retrieval field.