Bulletin of Electrical Engineering and Informatics
Vol 9, No 5: October 2020

Cowbree: A novel dataset for fine-grained visual categorization

Umar Akbar Khan (The University of Lahore)
Saira Moin U. Din (The University of Lahore)
Saima Anwar Lashari (Saudi Electronic University)
Murtaja Ali Saare (Universiti Utara Malaysia)
Muhammad Ilyas (University of Sargodha)

Article Info

Publish Date
01 Oct 2020


Fine-grained visual categorization (FGVC) dealt with objects belonging to one class with intra-class differences into subclasses. FGVC is challenging due to the fact that it is very difficult to collect enough training samples. This study presents a novel image dataset named Cowbreefor FGVC. Cowbree dataset contains 4000 images belongs to eight different cow breeds. Images are properly categorized under different breed names (labels) based on different texture and color features with the help of experts. While evidence shows that the existing dataset are of low quality, targeting few breeds with less number of images. To validate the dataset, three state of the art classifiers sequential minimal optimization (SMO), Multiclass classifier and J48 were used. Their results in term of accuracy are 68.81%, 55.81% and 57.45% respectively. Where results shows that SMO out performed with 68.81% accuracy, 68.4% precision and 68.8% recall.

Copyrights © 2020

Journal Info





Electrical & Electronics Engineering


Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the ...