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Camera Notes

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Camera Notes. Xian-Sheng Hua, Shipeng Li and Hong-Jiang Zhang. Microsoft Research Asia. ICME 2005 Amsterdam Netherlands July 7 2005. Motivation ... – PowerPoint PPT presentation

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Title: Camera Notes


1
Camera Notes
  • Xian-Sheng Hua, Shipeng Li and Hong-Jiang Zhang
  • Microsoft Research Asia

ICME 2005 Amsterdam Netherlands July 7 2005
2
Motivation
  • Taking notes is frequent in daily lives.
  • typically by pens
  • Other methods for taking notes
  • Digital ink
  • Typing
  • Camera (photo and video)
  • Audio recorder

3
Motivation
  • However
  • We frequently take, but seldom use
  • The main reason
  • Difficult to retrieve, though easy to take
  • A management and searching system is desirable
  • Collecting
  • Classification
  • Browsing / Searching
  • Quality enhancement
  • Exporting

4
Outline
  • System Overview
  • Camera Note Classification and Grouping
  • Quality Enhancement and Adjustment
  • Camera Note Management System
  • Experiments Conclusion

5
System Overview Camera Notes
  • Notes Photos and video clips

6
Camera Note Classification
  • Two steps
  • Note / Non-Note classification
  • Separate notes from normal media collection
  • Type classification
  • Maps, documents, slides, whiteboards, bulletins,

7
Note / Non-Note classification
  • Separate notes from normal media collection
  • They are generally mixed
  • It is time-consuming for manually classification
  • It is observed that for notes photos/clips
    typically
  • the edge density distribution is more uniform
  • the background are locally near monotony
  • color distribution is not balanced (only have
    several dominant colors)
  • no object motion and camera motion is mild

8
Note / Non-Note classification
  • Features we used
  • Edge Intensity Distribution n21 dim
  • Background Intensity Distribution n21 dim
  • Color Distribution 256 dim
  • Motion Intensity (for video clips) 1 dim
  • Typical SVM is applied for training and
    classification

9
Note Type classification
  • Except for the four features we also use
  • Edge Orientation Distribution 36 dim
  • Text Ratio 1 dim
  • Average Text Height 1 dim
  • Text Height Variance 1 dim
  • Classification method
  • Five one-versus-rest SVM classifiers
  • According to the largest positive distance from
    the five classifiers

10
Grouping and Linking
  • Group notes for the same item
  • By timestamp and similarity
  • Link to normal photos and/or video clips
  • Also by timestamp

11
Quality Adjustment / Enhancement
  • Automatically / Semi-automatically / Manually
  • Color/Brightness/Contrast Correction
  • Shape correction
  • Mosaic

12
Feature Extraction
  • Feature we may obtain
  • Histogram
  • Dominant Color
  • Text (by OCR)
  • User input
  • Keywords (may be input by users)
  • Voice description

13
Camera Notes System
  • XML is applied to describe the note library

14
Camera Notes System
  • XML is applied to describe the note library
  • Support the following functionalities
  • Note Importing
  • Sorting
  • Browsing
  • Editing
  • Searching
  • Exporting

15
Experiments for note classification
  • Data set
  • 5h video (895 shots)
  • 49 notes (shots)
  • 1300 photos (by DCs and camera phones)
  • 1072 notes (photos)
  • Random choose half for training and half for
    testing
  • And then switch

16
Experiments for note classification
17
Experiments for note classification
  • Misclassified samples Correct ? Classified

Map ? Document
Slides ? Whiteboard
Bulletin ? Document
18
Conclusion
  • An camera notes management system
  • based on image and video content analysis
  • This system enables efficient
  • importing, indexing, browsing, editing, searching
    and exporting

19
Future Works
  • To design more and better automatic and
    semi-automatic quality enhancement/adjustment
    schemes.
  • To improve the classification accuracy.
  • To support more note types.
  • To provide better user interface.
  • To enable more intuitive, convenient and accurate
    note searching.
  • To support exporting templates enabling flexible
    and personalized outputs.

20
Thank You
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