Popular content in video sharing web sites (e.g., YouTube) is usually duplicated. Most scholars define near-duplicate video clips (NDVC) based on non-semantic features (e.g., different image/audio quality), while a few also include semantic features (different videos of similar content). However, it is unclear what features contribute to the human perception of similar videos. Findings of two large scale online surveys (N = 1003) confirm the relevance of both types of features. While some of our findings confirm the adopted definitions of NDVC, other findings are surprising. For example, videos that vary in visual content –by overlaying or inserting additional information– may not be perceived as near-duplicate versions of the original videos. Conversely, two different videos with distinct sounds, people, and scenarios were considered to be NDVC because they shared the same semantics (none of the pairs had additional information). Furthermore, the exact role played by semantics in relation to the features that make videos alike is still an open question. In most cases, participants preferred to see only one of the NDVC in the search results of a video search query and they were more tolerant to changes in the audio than in the video tracks. Finally, we propose a user-centric NDVC definition and present implications for how duplicate content should be dealt with by video sharing websites.

1. Near-Duplicate Videos

2. Let’s say you’re looking for the Bush attack video…

3. … and you get 11,100 results.

4. … after 40 minutes... watching the videos listed on the first page you notice > 50% are similar, i.e. NDVC 27% in average [Wu et al., 2007]

6. … like this

8. Human Perception of Mauro Cherubini Rodrigo de Oliveira Nuria Oliver Near Duplicate Videos

9. What kind of NDVC? Malicious (i.e., spam produced by a single user) Copyright infringement (e.g., pirated music videos) User-edited content : videos that complement the original material with additional information

10. Recently NDVC detection algorithm

11. Recently NDVC detection algorithm

12. Why not? NDVC detection algorithm ?

15. Audio Quality NDVC Preference Stereo, 44 Khz Mono, 11 Khz

16. Image Quality NDVC Preference

17. Audio content (overlay) Preference NDVC

18. Visual + audio content (length) Preference Not NDVC

19. Visual content (editing) Not NDVC Want both

20. Similar semantics , different videos (similar visual info) NDVC Want both

21. Similar semantics , different videos (similar audio info) Not NDVC Preference

26. A Human-Centric stance in Multimedia research Biomimetics Crowdsourcing Psychophysical experiments

27. Thank you! Mauro Cherubini Rodrigo de Oliveira Nuria Oliver [email_address] [email_address] [email_address]