Investigation the Performance of Haralicks's Texture Features on Color Cooccurance Matrix for Image Retrieval
Many texture based image retrieval researches use global texture features for representing and retrieval of images from image database. Generally such researches suffer from misrepresentation of local information leading to inefficient image retrieval performance. This paper focuses on extracti...
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Main Authors: | , , , , |
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Format: | Conference or Workshop Item |
Language: | English |
Published: |
2014
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Subjects: | |
Online Access: | http://eprints.unisza.edu.my/411/1/FH03-FIK-14-02028.pdf http://eprints.unisza.edu.my/411/ |
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Institution: | Universiti Sultan Zainal Abidin |
Language: | English |
Summary: | Many texture based image retrieval researches use
global texture features for representing and retrieval
of images from image database. Generally such
researches suffer from misrepresentation of local
information leading to inefficient image retrieval
performance. This paper focuses on extracting local
Haralick’s texture feature based on predetermined
region using color co-occurrence matrix method
(CCM). Extensive experimental investigations
were conducted to determine the best out of eleven
Haralick’s texture features that will provide the
most efficient image retrieval performance based on
precision and recall criterion. Evaluations of the
retrieval performance were made based on 1000
selected images from Coral image database. From
the experimental findings, it is interesting to note
that for certain image categories, only six features
of the eleven Haralick’s texture features namely
homogeneity, sum of squares and sum average, sum
variance, difference entropy and information
measure correlation I provides the best image
retrieval performance. This finding has important
implication on the use of correct ‘contributed
features’ from Haralick texture features for certain
image properties as well as leading to less
computational processing time due to less
processing involved. |
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