Information Retrieval Techniques for non-textual media - Prof. A ...

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Text-based image retrieval : • Often, the image requester is able to give a verbal description/specification of the content of the required images. • These text-based queries can be <strong>for</strong>mulated as free-text or a term list that can be compared to such text descriptors as description, subjects, title and/or the text surrounding an embedded image, , using the text retrieval techniques.
Query by Image <strong>Retrieval</strong> (Content Based IR) • the term 'image content' refers to the implementation content of the image, i.e. to its pixel content. image, i.e. to its pixel content. • The objective of CBIR research and development activities is to develop automated routines that can analyze a digital image or image stream and identify the objects, actions and events that it portrays.
Page 1 and 2: INFORMATION RETRIEVAL TECHNIQUES FO
Page 3 and 4: Media Storage • Ninety-two percen
Page 5 and 6: Population Reference Bureau • the
Page 7 and 8: The WWW • contains about 170 tera
Page 9 and 10: Web content • The median size of
Page 11 and 12: AVI & mp3 files • In looking at f
Page 13 and 14: Growth of multimedia content • Th
Page 15 and 16: Databases • Collection of all org
Page 17 and 18: Metadata • The word Metadata is u
Page 19 and 20: Types of Search Queries • Most of
Page 21 and 22: THE MULTIMEDIA ANALYSIS AND RETRIEV
Page 23 and 24: Information Retrieval Systems (IRS)
Page 25 and 26: Ways of locating text documents •
Page 33 and 34: MIMD architectures • Document /da
Page 36 and 37: Multimedia Mining Hierarchy • Mul
Page 38 and 39: Image-based Applications • due to
Page 40 and 41: Images • Images offer a number of
Page 42 and 43: Characteristics of original image c
Page 44 and 45: The elements used for image represe
Page 46 and 47: Image retrieval queries • can be
Page 50 and 51: • Currently, CBIR routines are re
Page 52 and 53: Basic color profile • Though ther
Page 54 and 55: Automatic object recognition • To
Page 56 and 57: Automatic Linguistic Indexing of Pi
Page 60 and 61: Streamed media applications: Source
Page 62 and 63: Streamed-data applications include
Page 64 and 65: Search requests for streamed data
Page 66 and 67: Web Server vs. Streaming Server: CE
Page 68 and 69: Watch Live programmes
Page 70 and 71: • This media file is then placed
Page 72 and 73: Data Delivery • The rest of the s
Page 74 and 75: • For example, • One hour of vi
Page 76 and 77: Protocol issues • five major type
Page 78 and 79: Unicast protocols • send a separa
Page 80 and 81: Benefits of protocols • UDP - pro
Page 82 and 83: Vlc media player options
Page 84 and 85: Inputs VLC features page UDP Unicas
Page 86 and 87: Web 2.0 Ogg bitstream format • Fr
Page 88 and 89: Internet television: (ERNET Model)
Page 90 and 91: P2PTV • The term P2PTV refers to
Page 92 and 93: • The benefits of using PeerCast
Page 94 and 95: • Supported live media sources in
Page 96 and 97: Decoded capture • An approach use
Page 98 and 99:
• International Institute of Info
Page 100 and 101:
Music Information Retrieval • to
Page 102 and 103:
Content-based music retrieval using
Page 104 and 105:
Flash video + text + command button
Page 106 and 107:
E-content deployment • Full conte
Page 108 and 109:
LOR: Front page- Blocked content
Page 110 and 111:
Radio Broadcast TV Broadcast Videoc
Page 112:
Thank you
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