Modified Bag of Visual Words Model for Image Classification

  • Zainab Namh Sultani Computer Science Department, College of Science, Al-Nahrain University, Baghdad, Iraq
  • Ban N. Dhannoon Computer Science Department, College of Science, Al-Nahrain University, Baghdad, Iraq
Keywords: BoVW, Local features, SIFT, ORIB, Image classification

Abstract

Image classification is acknowledged as one of the most critical and challenging tasks in computer vision. The bag of visual words (BoVW) model has proven to be very efficient for image classification tasks since it can effectively represent distinctive image features in vector space. In this paper, BoVW using Scale-Invariant Feature Transform (SIFT) and Oriented Fast and Rotated BRIEF (ORB) descriptors are adapted for image classification. We propose a novel image classification system using image local feature information obtained from both SIFT and ORB local feature descriptors. As a result, the constructed SO-BoVW model presents highly discriminative features, enhancing the classification performance. Experiments on Caltech-101 and flowers dataset prove the effectiveness of the proposed method.

Published
2021-06-27
How to Cite
Sultani, Z. N., & N. Dhannoon, B. (2021). Modified Bag of Visual Words Model for Image Classification. Al-Nahrain Journal of Science, 24(2), 78-86. Retrieved from https://anjs.edu.iq/index.php/anjs/article/view/2402
Section
Articles