Evaluation of SIFT and SURF Using Bag of Words Model on a Large Dataset

Abstract

In this paper, the objective is image classification analysis based on the well known image descriptors, the Scale Invariant Feature Transform (SIFT) and the Speeded up Robust Features (SURF) on five online available standard datasets. For the classification framework, we adopted the visual words approach. For SIFT, we use the Lowe’s implementation and for Speeded up Robust Features (SURF), the Herbert Bay’s implementation is used. Extensive experimentation using five datasets shows that SURF is a better choice compared to Scale Invariant Feature Transform (SIFT).