using cross media correlation for scene detection in travel videos l.
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Using Cross-Media Correlation for Scene Detection in Travel Videos. Wei-Ta Chu , Che -Cheng Lin ,Jen-Yu Yu. Outline. Introduction Approach Experiments Conclusion. Introduction. Why Use Cross Media Correlation for Scene Detection in Travel Video??

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Presentation Transcript
outline
Outline
  • Introduction
  • Approach
  • Experiments
  • Conclusion
introduction
Introduction

Why Use Cross Media Correlation for Scene Detection in Travel Video??

What Correlation between photos and video?

More and more people get used to record daily life and travel experience both by Digital Cameras and Camcorders.

(much lower cost in Camera and Camcorders)

slide4

Why Use Cross Media Correlation for Scene Detection in Travel Video??

What Correlation between photos and video?

People often capture travel experience by still Camera and Camcorders.

Massive home videos captured in uncontrolled environments, such as overexposure/underexposure and hand shaking.

The content stored in photos and video contain similar information. Such as Landmark , Human’s Face.

why use cross media correlation for scene detection in travel video
Why Use Cross Media Correlation for Scene Detection in Travel Video??
  • It’s Hard for direct scene detection in video.
  • High correlation between photo and video.
  • Photo obtain high quality data (scene detection is more easier).
approach
Approach
  • What’s different purpose that people use photo and video even capture same things?
  • Photo

To obtain high quality data , capture famous landmark or human’s face

  • Video

To Capture evolution of an event

Utilize the correlation so that we can succeed the works that are harder to be conducted in videos, but easier to be done in photos

framework
FrameWork
  • To perform scene detection in photos:

First we cluster photo by checking time information.

  • To perform scene detection in videos:

First we extract several keyframe for each video shot, and find the optimal matching between photo and keyframe sequences

the proposed cross media scene detection framework
The proposed cross-media scene detection framework

Photos

Time-based clustering

Visual word representation

DP-based Matching

Scene

boundaries

Videos

Shot change detection

Filtering

(motion blur cease )

Visual word representation

Keyframe extraction

This process not only reduces the time of cross-media matching, but also eliminates the influence of bad-quality image

preprocessing
Preprocessing
  • Scene Detection for Photos

utilize different shooting time to cluster photo

denote the time difference between the ith photo and the (i+1)-th photo as gi

gi = ti+1-ti

K is an empirical threshhold

D is the size of sliding window

A scene change is claimed to occur between the nth and (n+1)-th photos. We set K as 17 and set d as 10 in this work.

preprocessing11
Preprocessing
  • Use Global k-means algorithm to extract Keyframe
  • Detect and Filtering blur Keyframe . It’s no only reduces the time of cross-media matching, but also eliminates the influence of bad-quality images.
visual word representation
Visual Word Representation
  • Apply the difference-of-Gaussian(DoG) detector to detect feature points in keyframes and photos
  • Use SIFT(Scale-Invariant Feature Transform) to describe each point as a 128-dimensional feature vector.
  • SIFT-based feature vectors are clustered by a k-means algorithm , and feature points in the same cluster are claimed to belong to the same visual word
visual word representation13
Visual Word Representation

KeyFrames , Photos

SIFT

Feature point (Feature vector)

K-means

Visual Word

visual word histogram matching
Visual Word Histogram Matching

Xi denote the i th prefix of X, i.e., Xi=<X1 ,X2,…, Xi>

LCS(Xi,Yj) denotes the length of the longest common subsequence between Xi and Yj

evaluation metric
Evaluation Metric

The first term indicates the fraction of the current evaluated scene, and the second term indicates how much a given scene is split into smaller scenes.

The purity value ranges from 0 to 1. Larger purity value means that the result is closer to the ground truth

τ(si ,sj*) is the length of overlap between the scene si and sj*

τ(si)is the length of the scene si

T is the total length of all scenes

slide17
Performance in terms of purity based on different numbers of visual words, with different similarity thresholds
conclusion
Conclusion

For video, extract keyframe by global k-means algo. (Scen spot can be easily determined by time information of photos)

Representingkeyframes and photo set by a sequence of visual word.

Transform scene detection into a sequence matching algo.

conclusion20
Conclusion
  • By using a dynamic programming approach , find optimal matching between two sequence, determine video scene boundaries with the help of photo scene boundaries.

By experiment on different travel video, different parameter settings, result shows that using correlation between different modalities is effective