Address:
Room No. 3164, Statistics Discipline, 3rd Academic Building, Khulna University, Khulna-9208, Bangladesh.
Email:
Contact:
+8801737-220964
Personal Webpage:
click hereMulti-Class Object Classification Using Diag-Rand HOG; A proposed hybrid approach
An object classification approach based on the hybridization of HOG and LBP was proposed to effectively classify features in complex environments, called Diag-Rand HOG. The efficiency of the proposed algorithm compared to the HOG descriptor was calculated on some well-known datasets like CIFER-10, Fashion-MNIST, and MNIST. We had got an expected outcome compared with other related works by the performance measuring tools like confusion matrix, Precision, Recall, F1-score and some accuracy indicatory graphs. We found the highest accuracy from our proposed model which is 99.2% from MNIST datasets, and 91.15% and 62.15% for Fashion-MNIST and CIFER-10 datasets respectively. Finally, we could hope that, as our feature descriptor along with CNN did a great performance. So, there remained a tremendous chance to work with this feature descriptor in real-life implementations like the renowned classifier and feature extractor.
Details | |||
Role | Supervisor | ||
---|---|---|---|
Class / Degree | Masters | ||
Students | Abubackar Siddique Sheba Student ID: 152040 Session: 2018-2019 | ||
Start Date | January, 2019 | ||
End Date | March, 2021 |