Spatial histogram of keypoints

Abstract

Among a variety of feature extraction approaches, special attention has been given to the SIFT algorithm which delivers good results for many applications. However, the non fixed and huge dimensionality of the extracted SIFT feature vector cause certain limitations when it is used in machine learning frameworks. In this paper, we introduce Spatial Histogram of Keypoints (SHiK), which keeps the spatial information of localized keypoints, on an effort to overcome this limitation. The proposed technique partitions the image into a fixed number of ordered sub-regions based on the Hilbert space- filling curve and counts the localized keypoints found inside each sub-region. The resulting spatial histogram is a compact and discriminative low-level feature vector that shows significantly improved performance on classification tasks. The proposed method achieves high accuracy on different datasets and performs significantly better on scene datasets compared to the Spatial Pyramid Matching method.