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Implementation of Gabor Filter for Carassius Auratus’s Identification

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@article{IJASEIT8128,
   author = {- Aristoteles and Yunda Heningtyas and Admi Syarif and AA Gieniung Pratidina},
   title = {Implementation of Gabor Filter for Carassius Auratus’s Identification},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {2},
   year = {2021},
   pages = {566--571},
   keywords = {Extraction feature; gabor filter; goldfish identification; pattern recognition; probability neural network.},
   abstract = {

Carassius Auratus, otherwise known as goldfish, is one of the most popular ornamental fish. Goldfish have many variations, such as differences in body shape, colors, size, and fins. Identifying goldfish manually is difficult to do. This is due to several species that have similar anatomy, so automatic fish identification is needed. This research aims to identify three species of goldfish, such as Fantail, Oranda, and Ranchu. Gabor filter was applied to extract the features of goldfish. Gabor filter consists of several steps, including parameter initialization, Gabor kernels, Gabor convolution, feature point. The parameters used were frequency, orientation, and kernel’s size. Gabor kernel was formed based on initialized parameters. The convolution process was produced by adding up the multiplication of 256x256 pixel goldfish’s images and Gabor kernels. The results of the convolution process were normalized to produce a feature vector matrix. A probability neural network was used to classify the goldfish. Probability Neural Network is a supervised network that finds its natural use in decision making and classification problems. This research used 216 of goldfish’s images. Seventy-two images were used for each species. The optimal parameters in this study were kernel size (5,5), frequency (3), orientation (5), and downsample (16,16), with accuracy up to 100%. Parameters of the frequency, orientation, kernel size, and downsample affect the level of accuracy. The greater the parameter value used, the more variations in feature vectors are obtained. Still, if too many variations of the feature vector cause redundancy data, it causes the classification process to be inefficient.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8128},    doi = {10.18517/ijaseit.11.2.8128} }

EndNote

%A Aristoteles, -
%A Heningtyas, Yunda
%A Syarif, Admi
%A Pratidina, AA Gieniung
%D 2021
%T Implementation of Gabor Filter for Carassius Auratus’s Identification
%B 2021
%9 Extraction feature; gabor filter; goldfish identification; pattern recognition; probability neural network.
%! Implementation of Gabor Filter for Carassius Auratus’s Identification
%K Extraction feature; gabor filter; goldfish identification; pattern recognition; probability neural network.
%X 

Carassius Auratus, otherwise known as goldfish, is one of the most popular ornamental fish. Goldfish have many variations, such as differences in body shape, colors, size, and fins. Identifying goldfish manually is difficult to do. This is due to several species that have similar anatomy, so automatic fish identification is needed. This research aims to identify three species of goldfish, such as Fantail, Oranda, and Ranchu. Gabor filter was applied to extract the features of goldfish. Gabor filter consists of several steps, including parameter initialization, Gabor kernels, Gabor convolution, feature point. The parameters used were frequency, orientation, and kernel’s size. Gabor kernel was formed based on initialized parameters. The convolution process was produced by adding up the multiplication of 256x256 pixel goldfish’s images and Gabor kernels. The results of the convolution process were normalized to produce a feature vector matrix. A probability neural network was used to classify the goldfish. Probability Neural Network is a supervised network that finds its natural use in decision making and classification problems. This research used 216 of goldfish’s images. Seventy-two images were used for each species. The optimal parameters in this study were kernel size (5,5), frequency (3), orientation (5), and downsample (16,16), with accuracy up to 100%. Parameters of the frequency, orientation, kernel size, and downsample affect the level of accuracy. The greater the parameter value used, the more variations in feature vectors are obtained. Still, if too many variations of the feature vector cause redundancy data, it causes the classification process to be inefficient.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8128 %R doi:10.18517/ijaseit.11.2.8128 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 2 %@ 2088-5334

IEEE

- Aristoteles,Yunda Heningtyas,Admi Syarif and AA Gieniung Pratidina,"Implementation of Gabor Filter for Carassius Auratus’s Identification," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 2, pp. 566-571, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.2.8128.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Aristoteles, -
AU  - Heningtyas, Yunda
AU  - Syarif, Admi
AU  - Pratidina, AA Gieniung
PY  - 2021
TI  - Implementation of Gabor Filter for Carassius Auratus’s Identification
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 2
Y2  - 2021
SP  - 566
EP  - 571
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Extraction feature; gabor filter; goldfish identification; pattern recognition; probability neural network.
N2  - 

Carassius Auratus, otherwise known as goldfish, is one of the most popular ornamental fish. Goldfish have many variations, such as differences in body shape, colors, size, and fins. Identifying goldfish manually is difficult to do. This is due to several species that have similar anatomy, so automatic fish identification is needed. This research aims to identify three species of goldfish, such as Fantail, Oranda, and Ranchu. Gabor filter was applied to extract the features of goldfish. Gabor filter consists of several steps, including parameter initialization, Gabor kernels, Gabor convolution, feature point. The parameters used were frequency, orientation, and kernel’s size. Gabor kernel was formed based on initialized parameters. The convolution process was produced by adding up the multiplication of 256x256 pixel goldfish’s images and Gabor kernels. The results of the convolution process were normalized to produce a feature vector matrix. A probability neural network was used to classify the goldfish. Probability Neural Network is a supervised network that finds its natural use in decision making and classification problems. This research used 216 of goldfish’s images. Seventy-two images were used for each species. The optimal parameters in this study were kernel size (5,5), frequency (3), orientation (5), and downsample (16,16), with accuracy up to 100%. Parameters of the frequency, orientation, kernel size, and downsample affect the level of accuracy. The greater the parameter value used, the more variations in feature vectors are obtained. Still, if too many variations of the feature vector cause redundancy data, it causes the classification process to be inefficient.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8128 DO - 10.18517/ijaseit.11.2.8128

RefWorks

RT Journal Article
ID 8128
A1 Aristoteles, -
A1 Heningtyas, Yunda
A1 Syarif, Admi
A1 Pratidina, AA Gieniung
T1 Implementation of Gabor Filter for Carassius Auratus’s Identification
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 2
YR 2021
SP 566
OP 571
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Extraction feature; gabor filter; goldfish identification; pattern recognition; probability neural network.
AB 

Carassius Auratus, otherwise known as goldfish, is one of the most popular ornamental fish. Goldfish have many variations, such as differences in body shape, colors, size, and fins. Identifying goldfish manually is difficult to do. This is due to several species that have similar anatomy, so automatic fish identification is needed. This research aims to identify three species of goldfish, such as Fantail, Oranda, and Ranchu. Gabor filter was applied to extract the features of goldfish. Gabor filter consists of several steps, including parameter initialization, Gabor kernels, Gabor convolution, feature point. The parameters used were frequency, orientation, and kernel’s size. Gabor kernel was formed based on initialized parameters. The convolution process was produced by adding up the multiplication of 256x256 pixel goldfish’s images and Gabor kernels. The results of the convolution process were normalized to produce a feature vector matrix. A probability neural network was used to classify the goldfish. Probability Neural Network is a supervised network that finds its natural use in decision making and classification problems. This research used 216 of goldfish’s images. Seventy-two images were used for each species. The optimal parameters in this study were kernel size (5,5), frequency (3), orientation (5), and downsample (16,16), with accuracy up to 100%. Parameters of the frequency, orientation, kernel size, and downsample affect the level of accuracy. The greater the parameter value used, the more variations in feature vectors are obtained. Still, if too many variations of the feature vector cause redundancy data, it causes the classification process to be inefficient.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=8128 DO - 10.18517/ijaseit.11.2.8128