Title: A comprehensive survey on the reduction of the semantic gap in content-based image retrieval

Authors: Jayant Jagtap; Nilesh Bhosle

Addresses: Symbiosis Institute of Technology (SIT), Symbiosis International (SIU) (Deemed University), Lavale, Pune, Maharashtra, India ' Department of Electronics and Telecommunication Engineering, D.Y. Patil College of Engineering, Ambi, Pune, Maharashtra, India

Abstract: In the last few decades, content-based image retrieval is considered as one of the most vivid research topics in the field of information retrieval. The limitation of current content-based image retrieval systems is that low-level features are highly ineffective to represent the semantic contents of the image. Most of the research work in content-based image retrieval is focused on bridging the semantic gap between the low-level features and high-level semantic concepts of image. This paper presents a thorough study of different techniques for the reduction of semantic gap. The existing techniques are broadly categorised as: 1) image annotation techniques to define the high-level concepts in image; 2) relevance feedback techniques to integrate user's perception; 3) machine learning and deep learning techniques to associate low-level features with high-level concepts. In addition, the general architecture of semantic-based image retrieval system has been discussed in this survey. This paper also highlights the current and future applications of content-based image retrieval. The paper concludes with promising future research directions.

Keywords: content-based image retrieval; CBIR; semantic gap; deep learning; image annotation; image retrieval; information retrieval; relevance feedback; survey.

DOI: 10.1504/IJAPR.2021.117207

International Journal of Applied Pattern Recognition, 2021 Vol.6 No.3, pp.254 - 271

Received: 08 Sep 2020
Accepted: 22 Feb 2021
Published online: 23 Aug 2021 *