Abstract

The widespread of smart devices along with the exponential growth of virtual societies yield big digital image databases. These databases can be counter-productive if they are not coupled with efficient Content-Based Image Retrieval (CBIR) tools. The last decade has witnessed the introduction of promising CBIR systems and promoted applications in various fields. In this article, a survey on state of the art content based image retrieval including empirical and theoretical work is proposed. This work also includes publications that cover research aspects relevant to CBIR area. Namely, unsupervised and supervised learning and fusion techniques along with low-level image visual descriptors have been reported. Moreover, challenges and applications that emerged to support CBIR research have been discussed in this work.

Highlights

  • The importance of digital image databases depends on how friendly and accurately users can retrieve images of interest

  • Shape attributes such as consecutive boundary segments, circularity, aspect ratio, moment invariant, Fourier descriptors, www.ijacsa.thesai.org eccentricity and orientation have been widely exploited to represent an image in Content-Based Image Retrieval (CBIR) systems [20, 97, 111]

  • Advanced researches proved that visual descriptors are unable to capture higher level semantic the user is interested in

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Summary

A Survey on Content-based Image Retrieval

Abstract—The widespread of smart devices along with the exponential growth of virtual societies yield big digital image databases. These databases can be counter-productive if they are not coupled with efficient Content-Based Image Retrieval (CBIR) tools. The last decade has witnessed the introduction of promising CBIR systems and promoted applications in various fields. A survey on state of the art content based image retrieval including empirical and theoretical work is proposed. This work includes publications that cover research aspects relevant to CBIR area. Unsupervised and supervised learning and fusion techniques along with lowlevel image visual descriptors have been reported. Challenges and applications that emerged to support CBIR research have been discussed in this work

INTRODUCTION
BRIDGING THE SEMANTIC GAP
Supervised and Unsupervised Learning
Multimodal Fusion and Retrieval
Colour Features
Texture Features
Shape Feature
Spatial Location
CBIR OFFSHOOTS
Automatic Image Annotation
Multiple Query-Based CBIR
Benchmarking
Query Formulation
Image Benchmark and Performance Measures
CONCLUSIONS

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