Indexing Still and Moving Images

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This presentation outlines the ways in which still images and video can be categorised to boost findability.

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Indexing Still and Moving Images

  1. 1. Indexing Still and Moving Images Ian Davis Global Taxonomy Delivery Manager Dow Jones Consulting Services © Copyright 2008 Dow Jones and Company | 1
  2. 2. Indexing Still and Moving Images Still image indexing Moving image indexing Two content types can often be treated very similarly Two content types also bring unique challenges © Copyright 2008 Dow Jones and Company | 2
  3. 3. Who uses still images?  Newspaper and magazine publishers  Book publishers  Internet or Intranet sites  Music labels  Advertising agencies  Design houses  Academics  Corporates  TV companies © Copyright 2008 Dow Jones and Company | 3
  4. 4. What do People Want? Access to images based on attributes: e.g. images photographed at a specific time. e.g. images photographed at a specific place. e.g. images created using a certain photographic process. © Copyright 2008 Dow Jones and Company | 4
  5. 5. What do People Want? Access to images based on specific depictions: e.g. 'sun loungers on a tropical beach‘ e.g. 'peacock, full tail display and head’ e.g. 'individuals or couples in modern, clean, bright kitchens or living rooms - ideally to show blur of motion.' © Copyright 2008 Dow Jones and Company | 5
  6. 6. What do People Want? Access to images based on conceptual terms: e.g. 'eerie images‘ e.g. 'exciting, speedy images’ e.g. 'images illustrating individuality' © Copyright 2008 Dow Jones and Company | 6
  7. 7. What do People Want? Access to images based on basic inherent content:  Colours  Textures  Shapes © Copyright 2008 Dow Jones and Company | 7
  8. 8. Fitting the system to the users  Image indexing should focus on the images and also on the people who will be looking for them. How images are be indexed should relate closely to this. © Copyright 2008 Dow Jones and Company | 8
  9. 9. Fitting the system to the users Editorial users often appreciate:  Full captions of two to three sentences  Detailed factual information Ad and design users often:  Care little for concrete facts  Like to browse for ideas  Like to search using concepts © Copyright 2008 Dow Jones and Company | 9
  10. 10. Indexing Strategy Choose from:  Manual approach  Automatic approach  Combined approach © Copyright 2008 Dow Jones and Company | 10
  11. 11. Fitting the system to the users  Understand client needs  Construct an approach to meet these needs  Choose from the available techniques and technologies  Consider textual or CBIR, or even textual and CBIR © Copyright 2008 Dow Jones and Company | 11
  12. 12. Methods of Image Classification and Retrieval Semantic content – textual classification and retrieval  Human analysis of images  Metadata development  Thesauri or ontology supported classification and retrieval Content based image retrieval (CBIR)  Automatic analysis of low level image attributes © Copyright 2008 Dow Jones and Company | 12
  13. 13. Manual Image Indexing Meta-date Coverage – layers of information  Technical image information  Basic image attributes  Image content information  Image context/aboutness information © Copyright 2008 Dow Jones and Company | 13
  14. 14. Manual Image Indexing Technical image information:  Unique scanning numbers  Scanner type  Processor name  Processing activities  Batch numbers  Sizes of image files © Copyright 2008 Dow Jones and Company | 14
  15. 15. Manual Image Indexing Basic Image Attributes:  Photographer name  Photography Types  Location photographed  Date photographed  Work type  Points of view © Copyright 2008 Dow Jones and Company | 15
  16. 16. Manual Image Indexing Image Content Information:  Actions and Events  Animals and Plants  People: roles, occupations, ethnicity, attributes, and anatomy  Topography  Built works  Things © Copyright 2008 Dow Jones and Company | 16
  17. 17. Manual Image Indexing Image context/aboutness information:  Emotions  Abstract concepts © Copyright 2008 Dow Jones and Company | 17
  18. 18. Automatic Image Indexing Inherent in image pixels:  Colours  Textures  Simple shapes © Copyright 2008 Dow Jones and Company | 18
  19. 19. Video Indexing Layers of information  Indexing the whole piece  Indexing the scene  Indexing the frame Types of information  Image information  Textual information  Audio information © Copyright 2008 Dow Jones and Company | 19
  20. 20. Video Indexing  Dealing with video includes all the challenges of still image indexing  Plus the technical issues of capturing textual data about the content  And the issue of automatically identifying scenes and frames  And the issue of capturing, indexing and linking audio soundtracks to these scenes and frames. © Copyright 2008 Dow Jones and Company | 20
  21. 21. Video Indexing  Much initial textual and audio data can be captured and indexed automatically  System can be trained to identify scene breaks and key frames can be selected  Focus is often on improving and monitoring the automated aspects and prioritising what work needs to be done manually – usually focusing on key frames/scenes in a piece © Copyright 2008 Dow Jones and Company | 21
  22. 22. Thank You! ian.davis@dowjones.com © Copyright 2008 Dow Jones and Company | 22

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