TY - JOUR ID - TI - Modified Bag of Visual Words Model for Image Classification AU - Zainab N. Sultani AU - Ban N. Dhannoon PY - 2021 VL - 24 IS - 2 SP - 78 EP - 86 JO - Al-Nahrain Journal of Science مجلة النهرين للعلوم SN - 26635453 26635461 AB - 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.

ER -