Project Explation PPT

EFFECTIVE AND EFFICIENT QUERY PROCESSING FOR

VIDEO SUBSEQUENCE IDENTIFICATION

BY MADHUKAR REDDY 08911A0516RAHUL P 08911A0532

PROJECT IN CHARGE-MR M.RAVI(HOD ,CSE)

Objective

o To find a generic database management solution towards effectively and efficiently searching similar videos, with tolerance to different variations introduced during not only transformation process but post-production editing.

o To retrieve similar frames from two videos

Why do we need this?

o To aid Recognition for Copyright Enforcement

o TV Commercial Detectiono Aid Investigating Agencieso Aid Film Certification Board

Introduction

o Rapid advances in multimedia and network technologies have popularized many applications using video databases

o A video sequence is an ordered set of a large number of frames

o Each frame is represented as a high-dimensional vector

How the existing system works?

o Content Based Video Retrieval o We need to check manually

whether a video is a part of a long stream by browsing its entire length

o Identification cannot be done in case of any editing in the video

o Time taking

Features of Proposed System

o Subsequence based video retrievalo Any subsequence of a long database

video that shares a similar content to a query clip is retrieved

o Relevant videos can be identified even if there exist transformation distortions, partial content reordering ,insertion, deletion or replacement

o Time efficient

Preferred Technologies

o Windows XP Operating Systemo JDK 6.0o Java Media Frameworko Edit Plus

Modules

oVideo Copy Detectiono Sliding of query video frame by frame on

database video with a fixed length windowo Globe signatures have been used to avoid

distortions while video transformationso Video is depicted globally o Applicable for queries with multiple shotso Detects videos of same temporal order and

length

oVideo Similarity Searcho Shortcomings of the former module are rectifiedo Sub sampled frame-based matching is done o Average inter frame similarity is taken into

considerationo Frame alignment, gap, noise for accurate

identification are also consideredo Scores of different factors are aggregated to

derive the most similar subsequence based on overall video similarity

Feasibility Study

o Is it worth doing ?o Technical Feasibilityo Operational Feasibilityo Economic Feasibility

Software Requirement Specification

o Spiral Model o Different stages of SDLCWhy Spiral Model?o Estimates become realistico Easy to cope with changes

Functional Requirements

o Input Videoo Database Videoo Conversion of Input Video into frameso Conversion of Output Video into

frameso Subsequence Identificationo Frames matchingo Show the duplicate frames from the

input video

Data Flow

Convert into No.of Frames

Select Input VideoSelect Database

Video

Sub Sequence IdentificationSub

SequenceYes

Show Subsequence Frames

Content Verification

Yes

Use Case Diagram

User

Select Input Video

Select Database Video

Sub sequence identification

Convert Both Video into No.of Frames

Shows the sub sequence frames

Sequence DiagramUser Input Database Frames sub sequence Duplicates

1 : input video() 2 : convert to frames()

3 : DB video()

4 : convert to frames()

5 : both frames()

6 : compare()

7 : duplicate frames()

Collaboration Diagram

UserInput

Database

Framessub sequence

Duplicates

1 : input video()

2 : convert to frames()

3 : DB video()

4 : convert to frames()

5 : both frames()

6 : compare()

7 : duplicate frames()

Class Diagram

Activity Diagram

Select Input Video Select DB Video

Convert to Frames

Sub sequence identification

Show duplicate frames

Testing

o Correctness, completeness, security and quality are achieved

o Manual testingUnit testingSystem TestingAcceptance TestingRegression Testing

o Operation testing is done by testing whether all components perform its intended operations

Implementation

o Java Swingso JLabels, JFrames,

JTextArea,JList,JFileChoosero Inherited from JComponent classo Pluggable look and feelo Javax.swing package

Snapshots

Future Enhancements

o Improvement of Video clarityo Elimination of distortionso Investigate the effect of representing

videos by other features, such as ordinal signature

o The weight of each factor for measuring video similarity might be adjusted by user feedback to embody the degree of similarity

Conclusion

o Similar frames are retrieved by algorithms

o Bipartite graph is constructedo Dense segments are choseno Irrelevant segments are prunedo Relevant segments are processed

Bibliography

o A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years, ”IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12,pp. 1349-1380, Dec. 2000.

o C. Faloutsos, M. Ranganathan, and Y. Manolopoulos, “Fast Subsequence Matching in Time-Series Databases,” Proc. ACM SIGMOD ’94, pp. 419-429, 1994.

o H. Wang, A. Divakaran, A. Vetro, S.-F. Chang, and H. Sun, “Survey of Compressed-Domain Features Used in Audio-Visual Indexing and Analysis,” J. Visual Comm. and Image Representation, vol. 14, no. 2, pp. 150-183, 2003.

o R. Mohan, “Video Sequence Matching,” Proc. IEEE Int’l Conf. Acoustics, Speech, and Signal Processing (ICASSP ’98), pp. 3697-3700, 1998.

o C. Kim and B. Vasudev, “Spatiotemporal Sequence Matching for Efficient Video Copy Detection,” IEEE Trans. Circuits and Systems for Video Technology, vol. 15, no. 1, pp. 127-132, 2005.

o X.-S. Hua, X. Chen, and H. Zhang, “Robust Video Signature Based on Ordinal Measure,” Proc. IEEE Int’l Conf. Image Processing (ICIP ’04), pp. 685-688, 2004.

Thank you!