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Comparison of different CBIR techniques

Authors: Meenakshi Madugunki; D. S. Bormane; Sonali Bhadoria; C. G. Dethe;

Comparison of different CBIR techniques

Abstract

Image retrieval is a poor stepchild to other forms of information retrieval (IR). Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. Color and texture features are important properties in content-based image retrieval systems. In this paper we have mentioned detailed classification of CBIR system. Also we have discussed about the efficiency of different techniques used in CBIR. We have compared different techniques as well as the combinations of them to improve the performance. We have also compared the effect of different matching techniques on the retrieval process.

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Average
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