Performance Comparison of
Target PNN Classification for Beef
and Pork Based on Gabor, PCA
and LBP Features
Lestari Handayani
[email protected]
Introduction
Issue :
•
In Indonesia, beef demand has reached 480.000 tonnes (DIT
JENNAK, 2012)…(increases every year)…
•
Sometimes vendors mix pork and beef…(more profit)
•
How can buyers to distinguish it?
•
Differences in color, texture, fiber, type of fat, and smell.
•
Solution: Image recognition based on color and texture features.
Feature Extractions
Mean of HSV
=
345,45
Mean of Texture
=
124,40
Color extraction
Texture extraction
Reseach Methodology
1.
Data collection : images of beef, pork and mixed
2.
Data analysis : data training, data testing
3.
Image identification process of beef, pork and a
mix of both :
HSV for color,
3 texture extraction methods (PCA, Gabor, LBP)
PNN clasification
4.
Design and Analysis System
Results and Analysis
Methods
Settings
Types of data
Average
Beef
Pork
Mix of
Conclusions
•
The texture and color features extracted from images can be
effectively used for identification of beef, pork and a mix of
both. This clearly shows the superiority of PCA+HSV+PNN over
LBP+HSV+PNN and Gabor+HSV+PNN.
•
The analysis has shown enhanced classification by selection of
spread values for each feature.
•
The recognizable mix of beef and pork is very good on
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