A complete texture image retrieval system includes two techniques: texture feature extraction and similarity measurement. Specifically, similarity measurement is a key problem for texture image retrieval study. In this paper, we present an effective similarity measurement formula. The MIT vision texture database, the Brodatz texture database, and the Outex texture database were used to verify t...

The unique iris structures motivate the development of an automatic identification system based on iris characteristic. Texture analysis with multichannel filtering approach is meant to extract feature from iris texture images and encode them into a compact and unique information for each iris. One of the tools that can analyze texture image in multiresolution approach is wavelet transform. In ...

This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level values of the raw pixels, as SVMs can generalize well even in high-dimensional spaces. Furthermore, it is shown that SVMs can incorporate conventional texture feature extraction methods within their own architec...

Surface properties such as cloudiness and granularity can not be easily measured in a quantitative way. Traditional computer vision methods such as thresholding-based object feature detectors do not provide sufficiently reliable information. On the other hand, most texture analysis methods are overwhelmingly complex and slow. This paper presents a technique that uses statistical texture measure...

In this paper we present an oriented texture feature set and apply it to the problem of offline writer identification. Our feature set is based on local binary patterns (LBP) which were broadly used for face recognition in the past. These features are inherently texture features. Thus, we approach the writer identification problem as an oriented texture recognition task and obtain remarkable re...

The problem of automatic classification of ultrasound images is addressed. For texture analysis of ultrasound images quantifiable indexes, called features, are used. Classification was performed using Gaussian mixture model based on Bayes classifier. The common problem of texture analysis is a feature selection for classification tasks. In this work we use genetic algorithms for a feature subse...

An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram s...

In this paper, we study texture discrimination based on two filter families, Gabor and Hermite, which agree with the Gaussian derivative model of the human visual system. In the first part, discrimination of different textures, based on the output energy of these filters, is compared using the Fisher criterion and classification result. Results show that the presented filter bank is suitable fo...

A distribution-based classification approach and a set of recently developed texture measures are applied to rotation-invariant texture classification. The performance is compared to that obtained with the well-known circular-symmetric autoregressive random field (CSAR) model approach. A difficult classification problem of 15 different Brodatz textures and seven rotation angles is used in exper...

Image metamorphosis as an animation tool has mostly been employed in the context of the entire image. This work explores the use of isolated and focused image based metamorphosis between two-dimensional objects, while capturing the features, colors, and textures of the objects. This pinpointed approach allows one to independently overlay several such dynamic shapes, without any bleeding of one ...