Rotation Invariant Texture Recognition Using a Steerable Pyramid
نویسنده
چکیده
A rotation-invariant texture recognition system is presented. A steerable orientedpyramid is used to extract representative features for the input textures. The steerability of the filter set allows a ship to an invariant representation via a DFT-encoding step. Supervised classijkation follows. State-of-the-art recognition results are presented on a 30 texture database with a comparison across the performance of the K-nn, Back-Propagation and Rule-Based classifiers. In addition, high accuracy estimation of the input rotation angle is demonstrated.
منابع مشابه
Learning How to Extract Rotation-Invariant and Scale-Invariant Features from Texture Images
Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power...
متن کاملInvariant Features for Texture Image Retrieval using Steerable Pyramid
In this paper, rotation, translation and luminance invariant features for texture image retrieval are investigated. The features are derived based on the statistical (standard deviation and shape parameter) distribution of the transform coefficients extracted from each steerable pyramid subband. By utilizing the proposed invariant features, the similarity measure between query and database imag...
متن کاملRotation-Invariant Texture Recognition
This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture ...
متن کاملRotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models
We present a new statistical model for characterizing texture images based on wavelet-domain hidden Markov models. With a small number of parameters, the new model captures both the subband marginal distributions and the dependencies across scales and orientations of the wavelet descriptors. Applying to the steerable pyramid, once it is trained for an input texture image, the model can be easil...
متن کاملLow-Complexity Rotation-Invariant Image Retrieval Based on Steerable Sub-Gaussian Modeling
This paper addresses issues that arise in the design of a rotation-invariant content-based image retrieval system. In our proposed procedure, we first construct a steerable multivariate sub-Gaussian model, which associates the fractional lower-order moments (FLOMs) of an image, transformed via a steerable pyramid, with those of its rotated versions. The feature extraction step consists of estim...
متن کامل