Efficient Object Detection and Matching Using Feature Classification

This paper presents a new approach for efficient object detection and matching in images and videos. We propose a stage based on a classification scheme that classifies the extracted features in new images into object features and non-object features. This binary classification scheme has turned out to be an efficient tool that can be used for object detection and matching. By means of this classification not only the matching process becomes more robust and faster but also the robust object registration becomes fast. We provide quantitative evaluations showing the advantages of using the classification stage for object matching and registration. Our approach could lend itself nicely to real-time object tracking and detection.