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See all Premium member Presentation Transcript Multimedia Data Mining : 6/6/2010 1 Multimedia Data Mining Multimedia Data Mining : Multimedia Data Mining Multimedia data types any type of information medium that can be represented, processed, stored and transmitted over network in digital form Multi-lingual text, numeric, images, video, audio, graphical, temporal, relational, and categorical data. 6/6/2010 2 Definitions : Definitions Subfield of data mining that deals with an extraction of implicit knowledge, multimedia data relationships, or other patterns not explicitly stored in multimedia databases Influence on related interdisciplinary fields Databases – extension of the KDD (rule patterns) Information systems – multimedia information analysis and retrieval – content-based image and video search and efficient storage organization 6/6/2010 3 Information model : Information model Data segmentation Multimedia data are divided into logically interconnected segments (objects) Pattern extraction Mining and analysis procedures should reveal some relations between objects on the different level Knowledge representation Incorporated linked patterns 6/6/2010 4 Generalizing Spatial and Multimedia Data : 6/6/2010 5 Generalizing Spatial and Multimedia Data Spatial data: Generalize detailed geographic points into clustered regions, such as business, residential, industrial, or agricultural areas, according to land usage Require the merge of a set of geographic areas by spatial operations Image data: Extracted by aggregation and/or approximation Size, color, shape, texture, orientation, and relative positions and structures of the contained objects or regions in the image Music data: Summarize its melody: based on the approximate patterns that repeatedly occur in the segment Summarized its style: based on its tone, tempo, or the major musical instruments played Similarity Search in Multimedia Data : 6/6/2010 6 Similarity Search in Multimedia Data Description-based retrieval systems Build indices and perform object retrieval based on image descriptions, such as keywords, captions, size, and time of creation Labor-intensive if performed manually Results are typically of poor quality if automated Content-based retrieval systems Support retrieval based on the image content, such as color histogram, texture, shape, objects, and wavelet transforms Multidimensional Analysis of Multimedia Data : 6/6/2010 7 Multidimensional Analysis of Multimedia Data Multimedia data cube Design and construct similar to that of traditional data cubes from relational data Contain additional dimensions and measures for multimedia information, such as color, texture, and shape The database does not store images but their descriptors Feature descriptor: a set of vectors for each visual characteristic Color vector: contains the color histogram MFC (Most Frequent Color) vector: five color centroids MFO (Most Frequent Orientation) vector: five edge orientation centroids Layout descriptor: contains a color layout vector and an edge layout vector Multi-Dimensional Analysis in Multimedia Databases : 6/6/2010 8 Color histogram Texture layout Multi-Dimensional Analysis in Multimedia Databases Mining Multimedia Databases : 6/6/2010 9 Refining or combining searches Search for “blue sky” (top layout grid is blue) Search for “blue sky and green meadows” (top layout grid is blue and bottom is green) Search for “airplane in blue sky” (top layout grid is blue and keyword = “airplane”) Mining Multimedia Databases Mining Multimedia Databases : 6/6/2010 10 Measurement Mining Multimedia Databases Classification in MultiMediaMiner : 6/6/2010 11 Classification in MultiMediaMiner Mining Associations in Multimedia Data : Mining Associations in Multimedia Data Associations between image content and non-image content features “If at least 50% of the upper part of the picture is blue, then it is likely to represent sky.” Associations among image contents that are not related to spatial relationships “If a picture contains two blue squares, then it is likely to contain one red circle as well.” Associations among image contents related to spatial relationships “If a red triangle is between two yellow squares, then it is likely a big oval-shaped object is underneath.” 6/6/2010 12 Slide 13: 6/6/2010 13 Special features: Need occurrences besides Boolean existence, e.g., “Two red square and one blue circle” implies theme “air-show” Need spatial relationships Blue on top of white squared object is associated with brown bottom Need multi-resolution and progressive refinement mining It is expensive to explore detailed associations among objects at high resolution It is crucial to ensure the completeness of search at multi-resolution space Mining Associations in Multimedia Data Mining Multimedia Databases : 6/6/2010 14 Spatial Relationships from Layout property P1 next-to property P2 property P1 on-top-of property P2 Mining Multimedia Databases Mining Multimedia Databases : 6/6/2010 15 From Coarse to Fine Resolution Mining Mining Multimedia Databases You do not have the permission to view this presentation. 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