Fast Features Invariant to Rotation and Scale of Texture
نویسندگان
چکیده
A family of novel texture representations called Ffirst, the Fast Features Invariant to Rotation and Scale of Texture, is introduced. New rotation invariants are proposed, extending the LBP-HF features, improving the recognition accuracy. Using the full set of LBP features, as opposed to uniform only, leads to further improvement. Linear Support Vector Machines with an approximate χ-kernel map are used for fast and precise classification. Experimental results show that Ffirst exceeds the best reported results in texture classification on three difficult texture datasets KTH-TIPS2a, KTH-TIPS2b and ALOT, achieving 88%, 76% and 96% accuracy respectively. The recognition rates are above 99% on standard texture datasets KTH-TIPS, Brodatz32, UIUCTex, UMD, CUReT.
منابع مشابه
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Conventional Gabor representation and its extracted features often yield a fairly poor performance in retrieving the rotated and scaled versions of the texture image under query. To address this issue, existing methods exploit multiple stages of transformations for making rotation and/or scaling being invariant at the expense of high computational complexity and degraded retrieval performance. ...
متن کاملA multi-scale and multi-orientation image retrieval method based on rotation-invariant texture features
Texture retrieval is a vital branch of content-based image retrieval. Rotation-invariant texture retrieval plays a key role in texture retrieval. This paper addresses three major issues in rotation-invariant texture retrieval: how to select the texture measurement methods, how to alleviate the influence of rotation for texture retrieval and how to apply the proper multi-scale analysis theory fo...
متن کاملLog-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
Classification of texture images, especially those with different orientation and scale changes, is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures. The rotation and scale invariant feature extraction for a given image involves applying a l...
متن کاملE cient Rotation Invariant Feature Extraction for Texture Segmentation - via Multiscale Wavelet Frames
This work presents an approach to the extraction of rotation invariant features for texture segmentation using multiscale wavelet frame analysis. The texture is decomposed into a set of bandpass channels by a circularly symmetric wavelet lter, which then gives a measure of edge magnitudes of the texture at di erent scales. The texture is characterized by local energies over small overlapping wi...
متن کاملA scale- and orientation-adaptive extension of Local Binary Patterns for texture classification
Local Binary Patterns (LBPs) have been used in a wide range of texture classification scenarios and have proven to provide a highly discriminative feature representation. A major limitation of LBP is its sensitivity to affine transformations. In this work, we present a scale- and rotation-invariant computation of LBP. Rotation-invariance is achieved by explicit alignment of features at the extr...
متن کامل