A comprehensive survey on the reduction of the semantic gap in content-based image retrieval. (13th August 2021)

• Record Type: Journal Article
• Authors: Jagtap, Jayant; Bhosle, Nilesh
• Is Part Of: International journal of applied pattern recognition. Volume 6:Number 3(2021)
• Page Start: 254
• Page End: 271
• Publication Date: 2021-08-13
• Subjects: content-based image retrieval -- CBIR -- semantic gap -- deep learning -- image annotation -- image retrieval -- information retrieval -- relevance feedback -- survey; Pattern perception -- Periodicals; Pattern recognition systems -- Periodicals; 006.405
• Languages: English
• ISSNs: 2049-887X

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.