A bootstrapping framework for annotating and retrieving WWW images
Proceedings of the 12th annual ACM international conference on Multimedia
General design algorithm for sparse frame expansions
Signal Processing
A shadow elimination approach in video-surveillance context
Pattern Recognition Letters
Shadow detection for moving objects based on texture analysis
Pattern Recognition
Denoising by sparse approximation: error bounds based on rate-distortion theory
EURASIP Journal on Applied Signal Processing
Toward autonomic grids: analyzing the job flow with affinity streaming
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Fuzzy Systems
Sound retrieval and ranking using sparse auditory representations
Neural Computation
Colour image coding with matching pursuit in the spatio-frequency domain
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Shadow detection: A survey and comparative evaluation of recent methods
Pattern Recognition
Visual detection of hexagonal headed bolts using method of frames and matching pursuit
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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A crucial problem in image analysis is to construct efficient low-level representations of an image, providing precise characterization of features which compose it, such as edges and texture components. An image usually contains very different types of features, which have been successfully modelled by the very redundant family of 2D Gabor oriented wavelets, describing the local properties of the image: localization, scale, preferred orientation, amplitude and phase of the discontinuity. However, this model generates representations of very large size. Instead of decomposing a given image over this whole set of Gabor functions, we use an adaptive algorithm (called matching pursuit) to select the Gabor elements which approximate at best the image, corresponding to the main features of the image. This produces compact representation in terms of few features that reveal the local image properties. Results proved that the elements are precisely localized on the edges of the images, and give a local decomposition as linear combinations of "textons" in the textured regions. We introduce a fast algorithm to compute the matching pursuit decomposition.