Keyframe Retrieval by Keypoints: Can Point-to-Point Matching Help?

Bag-of-words representation with visual keypoints has recently emerged as an attractive approach for video search. In this paper, we study the degree of improvement when point-to-point (P2P) constraint is imposed on the bag-of-words. We conduct investigation on two tasks: near-duplicate keyframe (NDK) retrieval, and high-level concept classification, covering parts of TRECVID 2003 and 2005 datasets. In P2P matching, we propose a one-to-one symmetric keypoint matching strategy to diminish the noise effect during keyframe comparison. In addition, a new multi-dimensional index structure is proposed to speed up the matching process with keypoint filtering. Through experiments, we demonstrate that P2P constraint can significantly boost the performance of NDK retrieval, while showing competitive accuracy in concept classification of broadcast domain.