Recent Advance in Content-based Image Retrieval: A Literature Survey

TL;DR

This survey reviews the progress in content-based image retrieval from 2003 to 2016, highlighting key techniques, challenges like semantic gap, and future research directions in the field.

Contribution

It categorizes and evaluates CBIR algorithms developed over 2003-2016, providing a comprehensive overview and identifying promising future research directions.

Findings

Various CBIR techniques have been developed addressing the semantic gap.

The survey categorizes algorithms into distinct groups based on features and methods.

Future research should focus on bridging the intention and semantic gaps.

Abstract

The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research.