Seminar on Academic Writing for International Publication and Presentation – Pusat Pengembangan Bahasa UIN Suska Riau

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Performance Comparison of

Target PNN Classification for Beef

and Pork Based on Gabor, PCA

and LBP Features

Lestari Handayani

lestari.handayani@uin-suska.ac.id

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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.}

(a). Beef

_{(b). Pork}

(c). Beef and

pork mixed

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Feature Extractions

Mean of HSV

=

345,45

Mean of Texture

=

124,40

Color extraction

Texture extraction

Today, I will compare 3 texture methods in PNN

classification

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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

5. Implementation : Java Programming Language

6. Testing

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Results and Analysis

Methods

Settings

Types of data

Average

Beef

Pork

Mix of

both

Gabor+HSV+

PNN

50% of data training :

50% of data testing,

spread=10

89.47% 94.44% 92.31%

92.08%

PCA+HSV+

PNN

All data extracted on

testing,

spread=10

90.90% 90.90%

100.00 %

93.93%

LBP+HSV+

PNN

90% of data training :

10% of data testing,

spread=10

75.00%

100.00

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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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