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Content based Image Retrieval using Interest Points and Texture Features

Scale. Scale. Scale 1. Scale 2. Scale 3. x-axis: the amplitude of the point itself y-axis: the amplitude of the neighbouring point (nearest neighbour Search). Content based Image Retrieval using Interest Points and Texture Features.

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Content based Image Retrieval using Interest Points and Texture Features

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  1. Scale Scale Scale 1 Scale 2 Scale 3 x-axis: the amplitude of the point itself y-axis: the amplitude of the neighbouring point (nearest neighbour Search) Content based Image Retrieval using Interest Points and Texture Features Christian Wolf 1, Jean-Michel Jolion 2, Walter G. Kropatsch 1, Horst Bischof 1 1Vienna University of Technology, Pattern Recognition and Image Processing Group http://www.prip.tuwien.ac.at 2 INSA de Lyon, Laboratoire Reconnaissance de Formes et Vision http://rfv.insa-lyon.fr Image representation by local Gabor features. Selection of locations with interest detectors (Harris, Jolion, Loupias) Scale 1 Scale 2 Scale 3 IP1 IP2 IP3 IP4 Representation I - Feature Vectors One feature vector per interest point Representation II - Histogram sets One Histogram per filter. Histograms model the amplitude distribution of this filter. Comparion using the Euclidean distance and compensation for small rotations A n-nearest neighbour search is performed for each interest point Final distance by number of corresponding interest points Test database 1: 609 Images taken from television. 568 used to query, grouped into 11 clusters: Upper limit Feature vect. Histograms Test database 2: 180 Images taken from various sources. Lower limit Performance Evaluation Precision of the query: H B F G J K (Part of test database 1) See demo at: http://www.prip.tuwien.ac.at/Research/ImageDatabases/Query This work was supported in part by the Austrian Science Foundation (FWF) under grant S-7002-MAT

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