oeagm2000_pres - Histograms for Texture Retrieval Christian...

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Histograms for Texture Retrieval Christian Wolf 1 Jean-Michel Jolion 2 Horst Bischof 1 1 Pattern Recognition and Image Processing, Vienna University of Technology Group Favoritenstr.9/1832, 1040 Wien, Austria. 2 INSA de Lyon, Laboratoire Reconnaissance de Formes et Vision 20, Avenue Albert Einstein, 69621 Villeurbanne cedex, France
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Tasks Content based image query: Retrieval of images from a database by specifying an example query image. The retrieved images must be similar according to a pre-defined image similarity measure. Examples: Video databases, web based search. .. Two Tasks: Find a suitable description for images Create a method to compare the images, i.e. find a distance for the descriptions.
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Interest points and Gabor features Interest regions Gabor filter bank Interest point detectors: Harris (corners) Jolion (Multi resolution Constrast based) Loupias (Haar & Daubechie wavelets) IP2 IP2 IP2 IP2 Or1 Or2 Or3 Or4 S1 S2 S3
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Creating histograms - 2 types x-axis: the amplitude of the point itself
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oeagm2000_pres - Histograms for Texture Retrieval Christian...

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