CBIR for classification of cow types using GLCM and color features extraction

Cow is one of the animals that have many benefits for humans. There are various types of cows based on benefits such as dairy cows, beef cattle, worker cattle, and others. Cattle breeding should be tailored to the needs of the public. Less knowledge about different types of cattle can reduce the ben...

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Bibliographic Details
Published in2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE) pp. 182 - 187
Main Authors Sutojo, T., Tirajani, Pungky Septiana, Ignatius Moses Setiadi, De Rosal, Sari, Christy Atika, Rachmawanto, Eko Hari
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2017
Subjects
CBIR
Color Feature Extraction
Correlation
Cow
Cows
Euclidean distance
Feature extraction
GLCM
Image Classification
Image color analysis
Training
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Summary:Cow is one of the animals that have many benefits for humans. There are various types of cows based on benefits such as dairy cows, beef cattle, worker cattle, and others. Cattle breeding should be tailored to the needs of the public. Less knowledge about different types of cattle can reduce the benefits of farmed cattle. Content Based Image Retrieval (CBIR) can be applied to help the problem of distinguishing or knowing the type of cow. The first step of the method proposed in this research is preprocessing by changing the background color, resizing and conversion of color space. Color feature extraction calculates the average and standard deviation of the color intensity of each color component. Next extract the texture feature using Gray Level Cooccurrence Matrix (GLCM) to look for contrast, energy, correlation, homogeneity and entropy at each angle 0 ° , 45 ° , 90 ° and 135 ° with a mean of 1 averaged. Six color features and five texture features are used as attributes to perform calculations with Euclidean Distance, so it can be known the similarity between images. Cattle types used include Limousin, Simental, Brangus, Peranakan Ongole (PO), and Frisien Holstein (FH). With 100 training images and 20 test images. To measure the accuracy of the proposed CBIR is used Confusion Matrix. Based on the measurement results obtained accuracy of 95% while the precision and recall obtained 100%.
DOI:10.1109/ICITISEE.2017.8285491