A Decade Survey of Content Based Image Retrieval Using Deep Learning

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.

I. Introduction

Image retrieval is a well studied problem of image matching where the similar images are retrieved from a database w.r.t. a given query image [1], [2]. Basically, the similarity between the query image and the database images is used to rank the database images in decreasing order of similarity [3]. Thus, the performance of any image retrieval method depends upon the similarity computation between images. Ideally, the similarity score computation method between two images should be discriminative, robust and efficient.