1. Chittampally Vasanth Raja 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. 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?? Motivation

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. 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. 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. 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. 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.

10. MATLAB Image acquisition tool

11. Extracted Y, Cb, Cr components from the given image Calculated the ICC formula Found an interesting point: The average of Y, Cb, Cr components values of an image are same even when the image is resized (anti aliasing) Extracted frames from the given video Can be able to save the frames into hard disk

12. Similarity search Connecting to the database Creating mentioned four tables

13. [1][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.htmlhttp://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=applicationhttp://www.mathworks.com/company/events/webinars/index. html?id=&language=en&by=application [6]http://www.mathworks.com/company/events/webinars/wbnr4 3666.html?id=43666&p1=723907038&p2=72390756http://www.mathworks.com/company/events/webinars/wbnr4 3666.html?id=43666&p1=723907038&p2=723907

14. Thank you