A Decade Survey of Content Based Image Retrieval Using Deep Learning

作     者：Dubey, Shiv Ram 

作者机构：Indian Inst Informat Technol Comp Vis Grp Chittoor 517646 India 

出 版 物：《IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY》 (IEEE Trans Circuits Syst Video Technol)

年 卷 期：2022年第32卷第5期

页      面：2687-2704页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：Global Innovation & Technology Alliance (GITA) on behalf of the Department of Science and Technology (DST)  Government of India [GITA/DST/TWN/P-83/2019] 

主　　题：Image retrieval Deep learning Measurement Taxonomy Internet Visualization Feature extraction Content based image retrieval deep learning CNNs survey supervised and unsupervised learning 

摘      要：The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.