S-SIFT: A Shorter SIFT without Least Discriminative Visual Orientation

Detection and description of local features are a classical problem in image processing and multimedia content analysis. Based on the inhomogeneity of visual orientation in human visual system, we propose a novel algorithm S-SIFT to detect and describe local image features. In three stages of SSIFT, the information from the least discriminability orientation is omitting. Compared with the standard SIFT algorithm, S-SIFT has lower dimension and provides a faster keypoint matching. Experiments on the standard dataset demonstrate that our algorithm yields comparable or even better results for feature detection and matching tasks. Keywords-visual orientation; real-world distribution; descriptors; scale-invariant feature transform