Fast and robust content-based copy detection based on quadrant of luminance centroid and adaptive feature comparison
Yusuke Uchida, Masayuki Hashimoto, Ryoichi Kawada · 2010
This paper proposes a fast and robust content-based copy detection scheme. Our proposal consists of a new compact feature, efficient keyframe selection and adaptive mask-based feature comparison. Firstly, a block-level luminance centroid is binarized into a 32-bit quadrant feature for fast and robust feature comparison. Subsequently, a new keyframe selection method is adopted to enhance pairwise independence between unrelated video segments in addition to choosing stable keyframes. Finally, a block-level mask-based feature comparison method is introduced to compare only stable features. Experimental results show our scheme improves recall by 0.1 at the same precision 0.9 and the processing speed in feature comparison of the proposed scheme is about twice as fast as that of conventional schemes.