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A content based image retrieval system for marine life images

Authors: Ahsan Raza Sheikh; M.H. Lye; Sarina Mansor; M. F. Ahmad Fauzi;

A content based image retrieval system for marine life images

Abstract

Malaysia has been recognized as one of the twelve nations endowed with rich biodiversity. Such huge number of species in the rain forest and sea are an important asset that need to be properly documented. Responding to these important needs, we have designed and evaluated a content based image retrieval system catered for marine life images. This paper investigates the effectiveness of various low level image descriptors, which includes the colour, shape and texture features in representing the semantic categories of the marine life images. This is a challenging task since the images are taken from different viewpoints and marine species do not possess consistent colour, shape and textural appearance. For the purpose of evaluating the overall image retrieval system effectiveness, we design and implement an image retrieval system which support image query by example. Experiment is conducted to evaluate the effectiveness of various low level image descriptors and the fusion of multiple features. The experiment result on the image retrieval performance is presented.

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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
Average
Top 10%
Top 10%
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