Towards Using Semantic Features for Near-Duplicate Video Detection

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Towards Using Semantic Features for Near-Duplicate Video Detection. Paper presented at the ICME 2010 Workshop on Visual Content Identification and Search in Singapore.

Towards using Semantic Features for Near-duplicate Video Detection Workshop on Visual Content Identification and Search Singapore – July 23, 2010 Hyun-seok Min , Wesley De Neve, Yong Man Ro Image and Video Systems Lab Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) email: hsmin@kaist.ac.kr

Outline semantic video signature 3. Experiments semantic concepts discussion methodology 4. Conclusions and future work background challenges 1. Introduction 2. Towards semantic-based NDVC detection /22

Background ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/22 Search results on YouTube for the query “I will survive Jesus” A significant number of search results are near-duplicates

NDVC Definition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/22

NDVC Examples /22 NDVC transformation (cam cording, subtitles) transformation (blur) original videos

Video Signature ,[object Object],[object Object],[object Object],[object Object],/22 video clip … video signature feature extraction

Video Signature based on Low-level Visual Features ,[object Object],[object Object],/22 original video NDVC transformation (cam cording, subtitles) … … video signature video signature Visual match? No!

Video Signature based on Semantic Features ,[object Object],[object Object],10/22 original video NDVC transformation (cam cording, subtitles) Semantic concepts: Semantic concepts: Semantic match? Yes! indoor, man, face, … indoor, man, face, …

Semantic Video Signature ,[object Object],[object Object],semantic video signature (binary-valued matrix) 1 1 0 1 1 sky ... 0 0 1 0 0 indoor ... keyframes (one for each shot) ... 0 1 0 0 1 architecture ... ...

Methodology (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/22

Methodology (2/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],14/22

Robustness Against Transformations (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],/22 Original frame Blurred frames using various strengths (filter size: 5, 9, 13, 17, 21)

Robustness Against Transformations (2/2) ,[object Object],[object Object],/22 similarity rates computed for our semantic video signature are about 90% for all transformations applied

Robustness Against Key Frame Selection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/22 CS CL SC EH HT Semantic features Variation 8.31 1.69 7.11 4.80 4.05 0.90

Uniqueness (1/2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/22

U niqueness (2/2) ,[object Object],[object Object],[object Object],[object Object],/22 NDVC detection performance increases as the number of shots in a query video sequence increases, even when a limited semantic concept vocabulary is in use N correct / N queries

Conclusions and Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],/22

We co-exist in an era, where tonnes and tonnes of videos are uploaded every day. Video copy detection has become the need for the hour as most of them are user generated Internet videos through popular sites such as YouTube. It acts as a medium to restrain piracy and prove whether the contents are legitimate. The usual procedure adopted in video copy detection techniques include discovering whether a query video is copied from a database of videos or not. This paper acquaints different Video copy detection techniques that have been adopted to ensure robust and secure videos along some applications of video fingerprinting.

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Towards Using Semantic Features for Near-Duplicate Video Detection

1. Towards using Semantic Features for Near-duplicate Video Detection Workshop on Visual Content Identification and Search Singapore – July 23, 2010 Hyun-seok Min , Wesley De Neve, Yong Man Ro Image and Video Systems Lab Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) email: hsmin@kaist.ac.kr

2. Outline semantic video signature 3. Experiments semantic concepts discussion methodology 4. Conclusions and future work background challenges 1. Introduction 2. Towards semantic-based NDVC detection /22

3. Outline semantic video signature 3. Experiments semantic concepts discussion methodology 4. Conclusions and future work background challenges 1. Introduction 2. Towards semantic-based NDVC detection /22

6. NDVC Examples /22 NDVC transformation (cam cording, subtitles) transformation (blur) original videos

9. Outline semantic video signature 3. Experiments semantic concepts discussion methodology 4. Conclusions and future work background challenges 1. Introduction 2. Towards semantic-based NDVC detection /22

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20. Outline semantic video signature 3. Experiments semantic concepts discussion methodology 4. Conclusions and future work background challenges 1. Introduction 2. Towards semantic-based NDVC detection /22

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22. Thank you! Any questions? /22

Towards Using Semantic Features for Near-Duplicate Video Detection

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