Classifying Images of Materials: Achieving Viewpoint and Illumination Independence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet leaders and bootstrap for multifractal analysis of images
Signal Processing
Viewpoint Invariant Texture Description Using Fractal Analysis
International Journal of Computer Vision
Extended fractal analysis for texture classification and segmentation
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Image classification often relies on texture characterization. Yet texture characterization has so far rarely been based on a true 2D multifractal analysis. Recently, a 2D wavelet Leader based multifractal formalism has been proposed. It allows to perform an accurate, complete and low computational and memory costs multifractal characterization of textures in images. This contribution describes the first application of such a formalism to a real large size (publicly available) image database, consisting of 25 classes of non traditional textures, with 40 high resolution images in each class. Multifractal attributes are estimated from each image and used as classification features within a standard k nearest neighbor classification procedure. The results reported here show that this Leader based multifractal analysis enables the effective discrimination of different textures, as performances in both classification scores and computational costs compare favorably against those of procedures previously proposed in the literature on the same database.