An Efficient Video Similarity Search Algorithm

1. An Efficient Video Similarity Search Algorithm ChittampallyVasanth Raja 10IT05F vasanthexperiments.wordpress.com

2. Introduction • With the rapid development of modern electronic equipment, the amount of multimedia data is increasing tremendously. • Now a days almost all the digital gadgets are coming with the in built camera in it. • Youtube itself contains trillions of videos and thousands of videos are posted every day all around the world.

3. Motivation • The rapid increase of multi media video data necessitates an efficient video similarity search • There are already many tag based search engines (relying only on tags not the exact content of video data) ex: Google, Bing, AltaVista, MSN, Yahoo Search etc., • It is a difficult task to retrieve multimedia data • More computation.. Can We Improve it??

4. To solve two challenging problems: 1) similarity measurement 2) search method • Similarity measurement: The video similarity is measured based on the calculation of the number of similar video components • search method: For the scalable computing requirement what search method do you employ? And What indexing mechanism do you employ?

5. An Efficient Video Similarity Search Algorithm • IDEA: • Feature extraction: by image characteristic code (ICC) based on the statistics of spatial temporal distribution. • Fast Search Approach: for scalable computing was presented based on clustering index table (CIT)

6. Feature Extraction • Video feature computation is generally based on image feature extraction. • Several low-level features such as color, texture, edge are usually adopted for image fingerprint. • It has been shown that YCbCr histogram is an effective video signature • Advantage: YCbCr coding is widely used in consumer electronic equipment such as TV, DVR and DVD etc

7. Feature Extraction(cont..) • The mean of YCbCr was employed for image feature computation • Where M and N are the width and height of image, respectively. Yij, Cbij,Crij stand for the value of Y, Cb and Cr components of each pixel

8. Feature Extraction(cont..) • For video similarity search and noise resistance, the mean statistics were four digits rounding off integers. • Image characteristic code (ICC) c is a joint feature representation made up of three statistical integers of every pixel components: Y, Cb and Cr. In this way, high dimensional feature was transformed into compact characteristic code and video similarity search can be implemented as text search.

9. Block Diagram/Flow Char

10. Implementation details • MATLAB (12 .m files) • Image acquisition tool • MS-ACCESS (1.mdb database) (4 tables) • Ffmpeg

11. Data base structure

12. Feature Extraction

13. Input Query

14. Retrieved Videos

15. Demo

16. References [1] An Efficient Video Similarity Search Algorithm. Zheng Cao, Ming Zhu. IEEE Transactions on Consumer Electronics, Vol. 56, No. 2, May 2010. [2] http://www.mathworks.com/help/toolbox/images/f12-12267.html [3] http://www.physicsforums.com/showthread.php?t=24029 [4] http://www.mathworks.com/products/viprocessing/ [5]http://www.mathworks.com/company/events/webinars/index.html?id=&language=en&by=application [6]http://www.mathworks.com/company/events/webinars/wbnr43666.html?id=43666&p1=723907038&p2=72390756

17. Thank you