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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.
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Near-Duplicate Videos
Let’s say you’re looking for theBush attack video…
…and you get
11,100 results.
…after40 minutes...
watching the videos listed on the first page you notice
> 50% are similar, i.e. NDVC27% in average [Wu et al., 2007]
NDVC technical definition
• Identical or approximately identical videos, that differ in some feature:– file formats, encoding parameters– photometric variations (color, lighting changes)– overlays (caption, logo, audio commentary)– editing operations (frames add/remove)– semantic similarity
NDVC are videos that are “essentially the same”NDVC are videos that are “essentially the same”
…like this
Two challenges:
1. There is no agreement on a single definition of NDVC
1. NDVC are mostly considered as redundant content that has to be removed from the system
Human Perception of
Mauro CherubiniRodrigo de Oliveira
Nuria Oliver
Near Duplicate Videos
What kind of NDVC?
Malicious (i.e., spamproduced by a single user)
Copyright infringement (e.g., pirated music videos)
User-edited content : videos that complement the original materialwith additional information
Recently
NDVC detection algorithm
Recently
NDVC detection algorithm
Why not?
NDVC detection algorithm
?
Methodology
• 2 large-scale online surveys (n=1003)• 7 pairs of NDVC (differing in 1 feature)
• Subjects were asked about:– Similarity– Preference
NDVC technical definition
• Identical or approximately identical videos, that differ in some features:– photometric variations (color, lighting changes)– overlays (caption, logo, audio commentary)– editing operations (frames add/remove)And …– semantic similarity (e.g., two deer grazing grass in two different forests)
Audio Quality
NDVCNDVC
PreferencePreference
Stereo, 44 Khz
Mono, 11 Khz
Image Quality
NDVCNDVC
PreferencePreference
Audio content (overlay)
PreferencePreference
NDVCNDVC
Visual + audio content (length)
PreferencePreference
Not NDVCNot NDVC
Visual content (editing)
Not NDVCNot NDVC
Want bothWant both
Similar semantics, different videos(similar visual info)
NDVCNDVC
Want bothWant both
Similar semantics, different videos(similar audio info)
Not NDVCNot NDVC
PreferencePreference
Implications for Design
1. User-centric NDVC definitionNDVC are approximately identical videos that might
differ in audio/image quality, or overlays. Conversely, identical videos with relevant complementary
information (changing clip length or scenes) are not considered as NDVC.
Furthermore, users perceive as near-duplicate videos that are not alike but that are visually similar and
semantically related.
NDVC are approximately identical videos that might differ in audio/image quality, or overlays. Conversely,
identical videos with relevant complementary information (changing clip length or scenes) are not
considered as NDVC.
Furthermore, users perceive as near-duplicate videos that are not alike but that are visually similar and
semantically related.
Implications for Design
2. Clustering– Groups sharing video,
audio, semantic content– Ranking based on
user-submitted query– Highlight the most
representative
Implications for Design
3. Feature and user adaptation– Boost ranking based on general observations
• More content• Better image/audio quality• …
– Boost ranking based on personalization• Abilities (e.g., auditory skills)• Task (e.g., video producer vs. movie enthusiastic)• Search query
Future Work
• NDVC’s differing in more than 1 low-level feature
• Propose ways to visualize the NDVCs• Study effects of user’s goals while searching
videos
A Human-Centric stance in Multimedia research
Biomimetics
Crowdsourcing
Psychophysical experiments
