Cowbree: A novel dataset for fine-grained visual categorization

Umar Akbar Khan, Saira Moin U. Din, Saima Anwar Lashari, Murtaja Ali Saare, Muhammad Ilyas


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.


Cowbree; Fined-grained visual categorization; J48; Multiclass; SMO; WEKA image filter package

Full Text:




  • There are currently no refbacks.

Bulletin of EEI Stats