6multimedia

6multimedia - CSE-5120-fall-2009 Indexing Multimedia...

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Unformatted text preview: CSE-5120-fall-2009 Indexing Multimedia Databases ? ? ? Problem definition : Multimedia System: A system that can store and retrieve objects/documents with text, voice, im- ages, animation, slides show etc. VOICE Query by content : Query based on features in the data content and not on a name or coding of the data file. E.g. query for a picture with a lot of red color, instead of a picture named ”XY0132”. • efficient and • complete (no false dismissals) Sample queries • primary key Find the employee record with emp = 123. • secondary key Find the employee records with salary = 40K. • text Find the documents containing the words mul- timedia, indexing . • 1-d signals Find companies with similar growth patterns. Find solar wind activity similar to today’s • 2-d signals Find photos that look like a sunset over the ocean. Find X-rays with a blob with tumor-like tex- ture. Find areas with cornfield colors • 3-d signals Find avg. brain scan of epileptics. • Spatial Queries Find the nearest restaurant from a given point Find all movie theaters within 5 miles from CUHK Find nearest restaurant with price < $15 for Won Ton Noodle • Video Find the moment of the end of a horse race. 36 Find the nearest restaurant from a given point: x x x x x x x x x Query (1,1) (5,4) Point P Sequential search: Calculate distance p (5- 1) 2 + (4- 1) 2 Return point with minimum distance Too slow! What did we do with conventional data search? B + tree 11 19 7 7 10 11 13 19 20 31 52 3 1 14 50 14 31 50 O(log n) If the question was: Is there a restaurant at a (5,4)? Yes we can do it. For example, if the data points are: (1,1), (1,9) (2,1), (3,1), (3,5), (4,0), (4,2), (4,9), (5,4), (6,2), (6,6), (6,9) (1,1) (1,9) (3,1) (4,2) (4,2) (4,9) (6,2) (6,2) (5,4) (6,6) (6,6) (6,9) (4,9) (3,5) (3,5) (4,0) (2,1) (2,1) x x x x x x x x x x ( 5 , 4 ) ( 1 , 5 ) x x Is there a restaurant at a (5,4)? (5,5)? Find the nearest restaurant to (1,5)? Near in first dimension, Far in second dimension. Find all restaurants within 3 km of (1,5) If we live in a one dimensional world: 1 3 6 7 10 11 13 14 15 18 x x x x x x x x x x 3 1 6 18 18 15 14 13 13 11 10 7 11 7 6 15 Find nearest restaurant to (11) Find nearest restaurant to (2). Find nearest restaurant to (17) At most 2 leaf nodes are searched. Find all restaurants within 2 km of (2). O(log N) Observation : 1. Mapping of data to multi-dimensional points. 2. Query: find near objects 3. Difficulty in handling such querying. 37 k-d Trees a b c d e f g h a b c d e f g h Nearest Neighbor query a b c d e f g h Q a b c d e f g h Range query a b c d e f g h Q a b c d e f g h 38 Indexing Multimedia Databases (Continue) For the spatial queries, e.g. find the nearest restaurant to a point, we find the restaurant whose Euclidean distances to the query point is the smallest....
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This note was uploaded on 04/23/2010 for the course CSC CSC5120 taught by Professor Adafu during the Fall '09 term at CUHK.

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6multimedia - CSE-5120-fall-2009 Indexing Multimedia...

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