Texture feature extraction is an important step in the facial expression recognition system. The traditional LBP method ignored the statistical characteristics of the texture change direction in the process of feature extraction, and we can extract more detailed texture information by the LDP method based on LBP, but the computational complexity is greatly increased. In order to extract more de...

Texture classification is used in various pattern recognition applications that possess feature-liked appearance. This paper aims to compile the recent trends on the usage of feature extraction and classification methods used in the research of texture classification as well as the texture datasets used for the experiments. The study shows that the signal processing methods, such as Gabor filte...

Image retrieval method utilizing texture information which is derived from Discrete Wavelet Transformation: DWT together with color information is proposed. One of the specific features of the texture information extracted from portions of image is based on Dyadic wavelet transformation with forming texture feature vector by using energy derived from Gabor transform on 7 by 7 pixel neighbor of ...

This paper presents a two-dimensional deformable model-based image segmentation method that integrates texture feature analysis into the model evolution process. Typically, the deformable models use edge and intensity-based features as the influencing image forces. Incorporation of the image texture information can increase the methods effectiveness and application possibilities. The algorithm ...

In this paper, Focus is on texture as primary feature. Shape and spatial information were secondary features. Texture features derived from nine grid sizes of independent and different Gabor filter banks were incorporated into the CBIR system by taking advantage of the fact that each grid size of filter is suited to capture particular set of localized frequency-images in diverse database. This ...

Recently, the research towards Brodatz database for texture classification done at considerable amount of study has been published, the effective classification are vulnerable towards for training and test sets. This study presents the novel texture classification method based on feature descriptor, called spatial cooccurrence with discrete shearlet transformation through the LPboosting classif...

BACKGROUND In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistic...

This paper presents a new simple and robust texture analysis feature based on Bidimensional Empirical Mode Decomposition (BEMD) and Local Binary Pattern (LBP). BEMD is a locally adaptive decomposition method and suitable for the analysis of nonlinear or nonstationary signals. Texture images are decomposed to several Bidimensional Intrinsic Mode Functions (BIMFs) by BEMD, which present a new set...

In this paper, a novel unsupervised segmentation framework for texture image queries is presented. The proposed framework consists of an unsupervised segmentation method for texture images, and a multi-filter query strategy. By applying the unsupervised segmentation method on each texture image, a set of texture feature parameters for that texture image can be extracted automatically. Based upo...