Genre-Adaptive Near-Duplicate Video Segment Detection

This paper proposes a fast and accurate method to detect all nearduplicate segments in a video stream. To reduce the computation time while ensuring the detection accuracy equivalent to that by brute-force frame-by-frame comparison, a two-step detection method is proposed; a fast but rough detection applied in a compressed feature vector space spanned by the result of a PCA, followed by confirmation of candidates in the original high dimension space. The results show that the proposed method accelerates the detection by more than 1,000 times while maintaining the detection accuracy. We also propose an entropy-based pixel selection scheme to generate feature vectors optimized for comparison of video segments within programs with mostly common pictures. The results show that the proposed scheme eliminates the false positives drastically, which should lead to even faster detection.

• Type: Conference
• Year: 2007
• Where: ICMCS
• Authors: Ichiro Ide, Kazuhiro Noda, Tomokazu Takahashi, Hiroshi Murase
• Keywords: Detection Accuracy, Feature Vectors, ICMCS 2007, Two-step Detection Method