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Multimedia

Multimedia. Multimedia/Video Search. Contents. Multimedia (MM) and search/retrieval Text-based MM search in General SEs Text-based MM search in Vertical SEs Tag-based MM search Content-based MM search. Multimedia Resources.

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Multimedia

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  1. Multimedia Multimedia/Video Search A. Frank

  2. Contents • Multimedia (MM) and search/retrieval • Text-based MM search in General SEs • Text-based MM search in Vertical SEs • Tag-based MM search • Content-based MM search A. Frank

  3. Multimedia Resources • – תמונות Pictures, Photos, Maps, Graphics,Animations • שמע – Audio, Sounds • מוסיקה– Music, MP3 • הקלטות – Radio, Podcasts, Speeches • חוזי– Video, Flash, Vlogs • סרטים – Movies, TV A. Frank

  4. MM Search & Information Retrieval (MMIR) • Search & information retrieval of multimedia objects is hard!! • Using SEs to find relevant, quality MM objects can be difficult and frustrating. • Sample query: find a movie that has a large mouse chasing a Persian cat with twins watching from above? • Common MM search is text-based; object text can be derived from: • caption of the MM object • text surrounding the MM object • filenames of MM object and the containing page • manual/automatic annotations • Recently, tag-based MM search is effective (social computing). • However, long due, content-based MM search on the Webis forthcoming. A. Frank

  5. Contents • Multimedia (MM) and search/retrieval • Text-based MM search in General SEs • Text-based MM search in Vertical SEs • Tag-based MM search • Content-based MM search A. Frank

  6. Multimedia: Google A. Frank

  7. Multimedia: Google A. Frank

  8. Multimedia: Yahoo A. Frank

  9. Yahoo Audio Search A. Frank

  10. A. Frank

  11. AOL Video Search A. Frank

  12. Multimedia: MSN Live A. Frank

  13. MSN Live A. Frank

  14. MSN Live Search A. Frank

  15. AltaVista Multimedia Search A. Frank

  16. Contents • Multimedia (MM) and search/retrieval • Text-based MM search in General SEs • Text-based MM search in Vertical SEs • Tag-based MM search • Content-based MM search A. Frank

  17. Images: Picsearch A. Frank

  18. Picsearch Advanced Interface A. Frank

  19. Audio: FindSounds A. Frank

  20. Types of Sounds A. Frank

  21. Video: SearchVideo A. Frank

  22. Video: Clipblast! • Organizes and makes the video Web relevant, fast and simple to navigate. • Video Search and Navigation technology makes it easy to search, browse and personalize the video that viewers want, when they want it. • Crawls the entire Web for all video content available, indexing more video content providers than any other video search engine. • Largest video distribution platform ever. A. Frank

  23. Video: Clipblast A. Frank

  24. Multimedia: Flurl A. Frank

  25. Flurl list of sites A. Frank

  26. Contents • Multimedia (MM) and search/retrieval • Text-based MM search in General SEs • Text-based MM search in Vertical SEs • Tag-based MM search • Content-based MM search A. Frank

  27. Flickr A. Frank

  28. Flickr A. Frank

  29. Flickr A. Frank

  30. Image/Maps: Panoramio A. Frank

  31. Image/Maps: Panoramio A. Frank

  32. Image/Maps: Panoramio A. Frank

  33. Video: YouTube A. Frank

  34. Keotag Meta-SE A. Frank

  35. Keotag Meta-SE A. Frank

  36. Contents • Multimedia (MM) and search/retrieval • Text-based MM search in General SEs • Text-based MM search in Vertical SEs • Tag-based MM search • Content-based MM search A. Frank

  37. “Content-based“ Image Search/Retrieval • “Content based” image DB or Web site(s)that contain a large number of images. • Image analysis techniques used to describe images by their extracted visual features such as color, texture, shape and orientation. • These objects can be represented by sets of image features and then compared with the sets extracted from other images. • Feature sets are described by their statistics, and the computer searches for statistically likely matches. • Queries can be formulated in terms of visual features or by similarity options. A. Frank

  38. Image: Squid • Has about 1100 images of marine creatures in its database. • Each image shows one distinct species on a uniform background. • Every image is processed to recover the boundary contour, which is then represented by three global shape parameters and the maxima of the curvature zero-crossing contours in its Curvature Scale Space (CSS) image. A. Frank

  39. Images: Squid A. Frank

  40. Images: Squid A. Frank

  41. Images: Squid A. Frank

  42. Images: Squid A. Frank

  43. Image: retriever • Experimental service which lets you search and explore in a selection of Flickr images by drawing a rough sketch. • Matches the most pronounced shapes and slabs of colors. • The results are usually fairly good, sometimes even stunning, • But it doesn't do object/face recognition of any kind, so if drawing an outline sketch of a chair, it almost certainly won't get you one back. A. Frank

  44. Images: retriever A. Frank

  45. Images: retriever A. Frank

  46. Images: retriever A. Frank

  47. MelodyHound A. Frank

  48. MelodyHound A. Frank

  49. MelodyHound A. Frank

  50. MelodyHound A. Frank

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