A Novel Scheme for Video Similarity Detection Chu-Hong Hoi, Wei Wang and Michael R. Lyu

1. The Chinese University of Hong Kong Int. Conf. on Image and Video Retrieval, Urbana, IL, USA, July 24-25,2003 Two-Phase Similarity Detection Framework Query Sample Video Databases Low-Level Feature Extraction Coarse Similarity Measure Based on Coarse Signature Fine Similarity Measure Based on Fine Signature Query Results Fig 2. Feature trajectories between two video sequences Experimental Results Fig 3. P-R curve of NPDH and FPDH methods Fig 4. P-R curve of NFT and NN methods A Novel Scheme for Video Similarity Detection Chu-Hong Hoi, Wei Wang  and Michael R. Lyu Dept. of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong, China • Two main challenges for video similarity detection • To find effective similarity measure metrics and techniques • To detect similar videos from large video databases efficiently • Our proposed solution • We present a new two-phase similarity detection scheme based on two kinds of signatures with different granularities: • Coarse and Fine. • Most of unrelated videos are filtered out with respect to the coarse similarity measure in the first phase. • In the second phase, the query sample is compared with the results from the first phase using the fine similarity measure. Filtering Coarse Similarity Measure • Coarse signature • Pyramid Partitioning and Density Histogram (Fig 1 (b)) • Naïve Pyramid Density Histogram (NPDH) • Fuzzy Pyramid Density Histogram (FPDH) • Coarse similarity Filtering • Filtering out unrelated videos based on the PDH signatures: Fig 1. (a) Regular Partition (b) Pyramid Partition PDH vector Fine Similarity Measure • Fine signature • Generating simplified video trajectories based on local similarity • Fine similarity measure • Dissimilarity measure between trajectories • Finding the query results based on the Nearest Feature Trajectories Conclusion: The proposed two-phase scheme can solve the video similarity detection effectively.