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Elearning - Rich Media Search
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Elearning - Rich Media Search

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As multimedia are being used to facilitate and enhance teaching and creative learning, making rich media including images and videos searchable are critical in today and future elearning platform and …

As multimedia are being used to facilitate and enhance teaching and creative learning, making rich media including images and videos searchable are critical in today and future elearning platform and management system. Traditionally, it's a difficult challenge. Some of the team members have invented an interdisciplinary framework which integrates the state-of-the-art image recognition technology and brain science research from MIT, intelligent cluster algorithm and patentable digital “tagging” technology

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  • Searchable video stream Non-linear video content access Crawlable - Video content -> text -> publish to web and crawled by search engine such as Google (and use Search Optimization technique) Dynamic - insert Ad (use image detection to detect sponsor images (e.g. ketchup) and dynamically insert link inside the video) Linkable - insert link (from one region of a frame in a clip to another frame in another clip – hypermedia) Unobtrusive - insert Text Ads to the clip on the side instead of a short clip at the opening
  • Transcript

    • 1. OMNIMEDIA “Intelligent Rich Media Search Platform”
    • 2. Searchable Media for E-Learning
      • As multimedia are being used to facilitate and enhance teaching and creative learning, making rich media including images and videos searchable are critical in today and future e-learning platform and management system. Traditionally, it's a difficult challenge. Some of the team members have invented an interdisciplinary framework which integrates the state-of-the-art image recognition technology and brain science research from MIT, intelligent cluster algorithm and patentable digital “tagging” technology.
    • 3. Solution
      • The solution is a community-based, video information-editing web platform, exposes and associates descriptive and searchable text with any digital video file. It is diverging from the traditional closed-environment manual-labor model and the fully artificial intelligent approach to metadata content generation. Instead, our solution is focusing on an automated solution to metadata generation through open, collaborative solutions.
    • 4. Technology
      • The technology is disruptive because a knowledge-sharing model for rich-media content management reduces maintenance costs and achieves higher production efficiency for metadata generation. The platform simultaneously captures users and learners activities and preferences on the content provider’s own website. It also facilitates incremental and collaborative learning through a open community.
    • 5. OMNIMedia Innovation
      • Artificial (Machine + Human)
      • Collective Intelligence Media search
      • Why?
      • Search strategy: User-centered approach,
        • High accuracy
        • More relevance (to user)
        • User added business value
        • Google gets it! (PageRank algorithm leverages user contribution)
      • Web Strategy: Ubiquitous
    • 6. Why better? Accuracy Hand inserted metadata Cost effectiveness Machine learning / Computer Vision OMNIMedia Platform
    • 7. Features highlights
      • Searchable video stream
      • Non-linear video content access
      • Crawlable by Search engine e.g. Google
      • Dynamic in video advertisement (use image detection to detect sponsor images)
      • Linkable - insert link (from one region of a frame in a clip to another frame in another clip)
      • Unobtrusive - Text or bubble Ads to the clip
    • 8. Example: MLB.com
    • 9. Object detection Cardeal.com Bookmark the Info without Leaving the video
    • 10. Youtube Video NYC Tiffany Bookmark the Info without Leaving the video
    • 11. Object detection Great Sushi from Porter Sq, Cambridge Bookmark the Info without Leaving the video
    • 12. Technology & Product
    • 13. Image Recognition Input image Texture Based Classification Tree / Not-Tree Classification Feature Vector Decision Input Image Shape Based Classification car car ped Biological Model Feature Extraction Car / Not-Car Classification Feature Vector Decision
    • 14. Image Recognition Performance Biological Model
    • 15. Auto-tagging for photographs
      • Landscape
      • Group Shot
      • Couple
      • Portrait
    • 16. Tagging assist
    • 17. Clustering – add contextual info http://eigencluster.csail.mit.edu/
    • 18. Interdisciplinary framework (hybrid solution) OMNIMedia Cluster Algorithm OMNIMedia platform Auto-generate Initial Metadata Add contextual Info to further enhance metadata User enriched Media content Metadata – fine Tune the image Recognition engine Computer Vision (Image recognition)
    • 19. Online Content Delivery Stack MySpace Operating System Browser ??? MLB.com YouTube etc G o o g l e WWW Internet
    • 20. Online Content Delivery Stack Semantic Web / Metadata layer Internet OS (Browser and OS converge) Search www Internet G o o g l e OMNIMedia MySpace MLB.com YouTube etc ?
    • 21. Technology Diffusion Web 2.0 services -> applications on InternetOS Data-driven G o o g l e OMNIMedia
    • 22. Value Chain

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