The document discusses the MPEG-7 Video Signature standard for content recognition. It aims to efficiently search large databases of videos by extracting compact and robust signatures that can detect duplicate, edited, or embedded video clips. The standard includes algorithms for signature extraction and matching, and enables interoperability across systems for content identification and management. It achieves high detection accuracy for common editing operations like embedding clips in longer videos.

1. The MPEG-7 Video Signature
Tools for Content Recognition

2. Introduction
What is MPEG-7?
• "Multimedia Content Description Interface“
• ISO/IEC standard by MPEG (Moving Picture Experts Group)
• Providing meta-data for multimedia
• MPEG-1, -2, -4: make content available;
MPEG-7: makes content accessible, retrievable, filterable,
manageable (via device / computer).
• Multi-degrees of interpretation of information’s meaning
• Support as broad a range of applications as possible.
• A compatible (with existing tech) and extensible standard.

3. Why The MPEG-7 Video
Signature Tools ?
• Handling billions of videos.
• Efficiently search for a copy of a specific
piece of video content.
• Duplicate video clip detection in large
databases.
• Finding out edited or modified version
and embedded in a longer piece of video
content.

4. Application’s domains (incomplete)
• Broadcast media selection (e.g., radio channel, TV channel).
• Digital libraries (e.g., film, video, audio and radio archives).
• E-Commerce (e.g., personalized advertising).
• Education (e.g., repositories of multimedia courses, multimedia
search for support material).
• Home Entertainment (e.g., management of personal multimedia
collections, including manipulation of content, e.g. karaoke).
• Journalism (e.g. searching speeches of a certain politician using his
name, his voice or his face).
• Multimedia directory services (e.g. yellow pages, G.I.S).
• Surveillance and remote sensing.
• Database management and deduplication

5. Requirements of the proposed
technology
1) Uniqueness
2) Robustness to editing operations
3) Independence
4) Fast matching
5) Fast extraction
6) Compactness
7) Non alteration of the content
8) Self-containment of the signatures
9) Coding independence
10)Partial matching
11)Accurate temporal localization of duplicated and
12)embedded content

6. Scope
There are four parts to the standard in video signature:
1) The descriptor extraction and decoding, along with its
descriptor definition language
2) A reference software implementation and source code
for the video signature tools
3) The conditions and dataset for ensuring conformance to
the standard
4) An exemplary pair wise matching and localization
scheme, as used during the MPEG-7 evaluation
process

7. Main benefits to the different
systems
1) Follows a systematic peer-reviewed evaluation
process, leading to the adoption of the best
technologies from various proposals
2) the video signature tools enable
interoperability, i.e., they allow different users and
systems to talk to each other in terms of descriptors.

8. Video Signature Extraction and
Compression
The video signature comprises two parts:
1) Fine signatures
2) Coarse signatures
3) Video Signature Organization

9. Video Signature Matching and
Localization
• The matching between two video signatures v1 and v2 is carried out
in three stages
.
1) Coarse Signature Matching
2) Temporal Parameter Estimation
3) Localization and Verification

10. Hardware Specification
Minimum Required Configuration:
• Intel Pentium or equivalent processor
• 128 MB RAM
• TEN GB HDD
• Keyboard (104 keys/…), Mouse(2/3 buttons)
• Svga color/black & white

11. Conclusion
• Achieves high levels of robustness to
common video editing operations
• Accurately detect and localize a piece of
video content embedded in a longer piece
of unrelated video content

12. Bibliography
1) Stavros Paschalakis, Kota Iwamoto, Paul Brasnett, Nikola Sprljan, Ryoma
Oami, Toshiyuki Nomura, Akio Yamada, Miroslaw Bober, “The MPEG-7 Video
Signature Tools for Content Identification”, IEEE TRANSACTIONS ON CIRCUITS
AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 22, NO. 7, JULY 2012.
2) A. Hampapur, K. Hyun, and R. Bolle, “Comparison of sequence matching
techniques for video copy detection,” in Proc. Conf. Storage Retrieval Media
Databases, 2002, pp. 194–201.
3) J. Law-To, L. Chen, A. Joly, I. Laptev, O. Buisson, V. Gouet-Brunet, N.
Boujemaa, and F. Stentiford, “Video copy detection: A comparative study,” in Proc.
6th ACM Int. Conf. Image Video Retrieval, Jul. 2007, pp. 371–378.
4) H. T. Shen, X. Zhou, Z. Huang, J. Shao, and X. Zhou, “UQLIPS: A real-time near-
duplicate video clip detection system,” in Proc. 33rd Int. Conf. Very Large Data
Bases, Sep. 2007, pp. 1374–1377.
5) S. Paisitktiangkrai, T. Mei, J. Zhang, and X.-S. Hua, “Scalable clip-based near-
duplicate video detection with ordinal measure,” in Proc. ACM Int. Conf. Image
Video Retrieval, Jul. 2010, pp. 121–128.
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13. THANK YOU