A Decade Survey of Content Based Image Retrieval using Deep Learning

TL;DR

This survey reviews a decade of deep learning advancements in content-based image retrieval, highlighting how automatic feature learning has replaced hand-designed descriptors and analyzing various state-of-the-art methods.

Contribution

It provides a comprehensive categorization and performance analysis of deep learning-based image retrieval methods over the past decade.

Findings

Deep learning has become the dominant approach for image feature extraction.

Various supervised and unsupervised deep learning models have been applied.

Performance improvements have been significant across different retrieval tasks.

Abstract

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.