CMPT-884

Jan 18, 2010

Video Copy Detectionusing Hadoop

Presented by: Cameron Harvey

Naghmeh Khodabakhshi

CMPT 820

December 2, 2010

Introduction

Video Copy Detection is an important tool for detecting copyright infringements

A copy can be obtained through a set of transformations from the original such as: Video cropping or scaling Gamma shift Blurring Addition of logo Changes in quality (noise, framerate, …)

Copy Detection Process

There are 2 stages in the Copy Detection Process Signature Extraction

The video content is used to create a unique signature of the reference and the query video

Signature matching A distance metric compares the signatures of the

videos If they are close enough, the query is considered to be

a copy

SURF - Speeded up Robust Features

A method to find and extract a set of interest points from an image

It is based on SIFT – Scale Invariant Feature Transformation

Our Solution

We implemented the Copy Detection algorithm of Roth et. al. [1] but parallelized the process to speed up processing

For each frame in the video we divide the image into 4x4 giving 16 regions

[1] G. Roth, R. Lagani`ere, P. Lambert, I. Lakhmiri, and T. Janati. A simple but effective approach to video copy detection. In CRV ’10: Proceedings of the 2010 Canadian Conference on Computer and Robot Vision, pages 63–70, Washington, DC, USA, 2010. IEEE Computer Society.

Our Solution (2)

A signature for each frame is created by counting the number of SURF features discovered in each region. The signature is a 16-dimensional vector

We compare frames using a distance metric

If the distance is below a threshold, then the frames are considered to be a match

Our Solution (3)

A video is considered a copy if there are a significant number of consecutive matching frames

Hadoop: Map-Reduce

Based on key-value data structures

MAP The input to the MAP function is a list of key-value

pairs. A user defined function is applied to every element of

the list REDUCE

The output of the Map is sorted based on the keys and passed to the Reduce function

The Reduce function joins values with the same keys

Parallelizing the Process

Parallelizing the Process (2)

Results: Gamma Correction

Results: Scaling

Results: Blurring

Results: Hadoop

Signature extraction using a single node 1 hour 2 minutes and 11 seconds

Signature extraction using 6 nodes 12 minutes and 3 seconds

Questions