An Efficient Video Similarity Search Algorithm

Chittampally Vasanth Raja10IT05Fvasanthexperiments.wordpress.com

An Efficient Video Similarity Search AlgorithmIntroductionWith 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.

The rapid increase of multi media video data necessitates an efficient video similarity searchThere 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 dataMore computation.. Can We Improve it??

MotivationTo 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 componentssearch method: For the scalable computing requirement what search method do you employ? And What indexing mechanism do you employ?

An Efficient Video Similarity Search AlgorithmIDEA: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)

The similarity of video was measuredaccording to the computation of the number of similar videocomponents. Besides, a fast search approach for scalablecomputing was presented based on clustering index table(CIT)5Feature ExtractionVideo 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 signatureAdvantage: YCbCr coding is widely used in consumer electronic equipment such as TV, DVR and DVD etcFeature 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

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.Block Diagram/Flow Char

Implementation detailsMATLAB (12 .m files)Image acquisition toolMS-ACCESS (1.mdb database) (4 tables)Ffmpeg

Data base structure

Feature Extraction

Input Query

Retrieved Videos

DemoReferences[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 Thank you