Recent Advance in Content-based Image Retrieval: A Literature Survey
It provides a literature review for researchers in computer vision and image retrieval, but it is incremental as it summarizes existing work without new results.
This paper surveys content-based image retrieval (CBIR) techniques from 2003 to 2016, addressing challenges like the intention and semantic gaps, and categorizes algorithms while suggesting future research directions.
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.