Texture classification with modified rotation invariant local binary pattern and gradient boosting

Since texture is prominent low level feature of an image, most of the image processing and computer vision applications rely on this feature for efficient extraction, retrieval, visualization and classification of the images. Hence, the texture analysis method mainly concentrates on efficient feature extraction and representation of the image. The images captured and analyzed in many of the applications are not in same (or) similar scale, orientation and illumination and also texture has regular, stochastic, periodic, homogeneous (or) inhomogeneous and directional in nature. To address these issues, recent texture analysis method focused on efficient and invariant feature extraction and representation with reduced dimension. Hence this paper proposes a invariant texture descriptor, Locality preserving Rotation Invariant Modified Directional Local Binary Pattern (LRIMDLBP) based on LBP. The classical LBP descriptor is widely used in most of the texture analysis applications due to its simplicity and robustness to illumination changes. However, it does not efficiently capture the discriminative texture information because it uses sign information and ignores the magnitude value of the neighborhood and also suffers from high dimensionality. Hence to improve the performance of LBP, many variants are proposed. Though most of these LBP variants are either geometrical or direction invariant, fails to address the spatial locality and contrast invariance. To address these issues, the proposed LRIMDLBP incorporates spatial locality, contrast and direction information for rotation invariant texture descriptor with reduced dimension. The proposed LRIMDLBP consists of 5 phases: (i) Reference point identification, (ii) Magnitude calculation, (iii) Binary Label computation based on threshold, (iv) Pattern identification in dominant direction and (v) LRIMDLBP code computation. The locality and rotation invariance is achieved by identifying and using reference point in a local neighborhood. The reference point is a dominant pixel whose magnitude is large in the neighborhood excluding center pixel. The spatial locality and rotation invariance is achieved by preserving the weights of LBP dynamically based on the reference point. The proposed method also preserves the direction information of the texture by comparing the magnitude of the pixel in the four dominant directions such as horizontal, vertical, diagonal and anti-diagonal directions. Finally the proposed invariant LRIMDLBP descriptor computes histogram based on decimal pattern value. The proposed LRIMDLBP descriptor results in texture feature with reduced dimension when compared to other LBP variants. The performance of the proposed descriptor is evaluated with large and well known four bench mark texture datasets namely (i) CUReT, (ii) Outex, (iii) KTS-TIPS and (iv) UIUC against three classifiers such as (i). K-Nearest Neighbor (K-NN), (ii). Support Vector Machine (SVM) with Radial Basis Function (RBF) and (iii). Gradient Boosting Classifier (GBC). The intensive experimental result shows that the ensemble based GBC yields superior classification accuracy of 99.38%, 99.43%, 98.67% and 98.82% for the datasets CUReT, Outex, KTH-TIPS and UIUC respectively when compared with other two classifiers and also improves the generalization ability. The proposed LRIMDLBP descriptor achieves approximately 15% more classification accuracy when compared with traditional LBP and also produces 1% to 2.5% more classification accuracy compared with other state of the art LBP variants.

Advanced Microtexture Study of Anacardium occidentale L. Leaf Surface From the Amazon by Fractal Theory

This work applies stereometric parameters and fractal theory to characterize the structural complexity of the 3D surface roughness of Anacardium occidentale L. leaf using atomic force microscopy (AFM) measurements. Surface roughness was studied by AFM in tapping mode, in air, on square areas of 6,400 and 10,000 μm2. The stereometric analyses using MountainsMap Premium and WSXM software provided detailed information on the 3D surface topography of the samples. These data showed that the morphology of the abaxial and adaxial side of the cashew leaf is different, which was also observed in relation to their microtextures. Fractal analysis showed that the adaxial and abaxial sides have strong microtexture homogeneity, but the adaxial side presented higher surface entropy. These results show that image processing associated with fractal theory can be an indispensable tool for identifying plant species by their leaves because this species has singularities on each side of the leaf.

Multi-focus image fusion using fractal dimension

Multi-focus image fusion is defined as "the combination of a group of partially focused images of a same scene with the objective of producing a fully focused image." Normally, transform-domain-based image fusion methods preserve the textures and edges in the blend image, but many are translation variant. The translation-invariant transforms produce the same size approximation and detail images, which are more convenient to devise the fusion rules. In this work, a translation-invariant multi-focus image fusion approach using the à-trous wavelet transform is introduced, which uses fractal dimension as a clarity measure for the approximation coefficients and Otsu's threshold to fuse the detail coefficients. The subjective assessment of the proposed method is carried out using the fusion results of nine state-of-the-art methods. On the other hand, eight fusion quality metrics are considered for the objective assessment. The results of subjective and objective assessment on grayscale and color multi-focus image pairs illustrate that the proposed method is competitive and even better than some of the existing methods.

Application of quantum-behaved particle swarm optimization to motor imagery EEG classification

In this study, we propose a recognition system for single-trial analysis of motor imagery (MI) electroencephalogram (EEG) data. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system chiefly consists of automatic artifact elimination, feature extraction, feature selection and classification. In addition to the use of independent component analysis, a similarity measure is proposed to further remove the electrooculographic (EOG) artifacts automatically. Several potential features, such as wavelet-fractal features, are then extracted for subsequent classification. Next, quantum-behaved particle swarm optimization (QPSO) is used to select features from the feature combination. Finally, selected sub-features are classified by support vector machine (SVM). Compared with without artifact elimination, feature selection using a genetic algorithm (GA) and feature classification with Fisher's linear discriminant (FLD) on MI data from two data sets for eight subjects, the results indicate that the proposed method is promising in brain-computer interface (BCI) applications.

SINGLE-TRIAL MOTOR IMAGERY CLASSIFICATION USING ASYMMETRY RATIO, PHASE RELATION, WAVELET-BASED FRACTAL, AND THEIR SELECTED COMBINATION

An electroencephalogram (EEG) analysis system is proposed for single-trial classification of motor imagery (MI) data in this study. Applying event-related brain potential (ERP) data acquired from the sensorimotor cortices, the system mainly consists of enhanced active segment selection, feature extraction, feature selection and classification. In addition to the original use of continuous wavelet transform (CWT) and Student's two-sample t-statistics, the 2D anisotropic Gaussian filter is proposed to further refine the selection of active segments. We then extract several features, including spectral power and asymmetry ratio, coherence and phase-locking value, and multiresolution fractal feature vector, for subsequent classification. Next, genetic algorithm (GA) is used to select features from the combination of above-mentioned features. Finally, support vector machine (SVM) is used for classification. Compared with "without enhanced active segment selection," several potential features and linear discriminant analysis (LDA) on MI data from two data sets for 10 subjects, the results indicate that the proposed method achieves 86.7% average classification accuracy, which is promising in BCI applications.

APPLICATION OF COMPETITIVE HOPFIELD NEURAL NETWORK TO BRAIN-COMPUTER INTERFACE SYSTEMS

We propose an unsupervised recognition system for single-trial classification of motor imagery (MI) electroencephalogram (EEG) data in this study. Competitive Hopfield neural network (CHNN) clustering is used for the discrimination of left and right MI EEG data posterior to selecting active segment and extracting fractal features in multi-scale. First, we use continuous wavelet transform (CWT) and Student's two-sample t-statistics to select the active segment in the time-frequency domain. The multiresolution fractal features are then extracted from wavelet data by means of modified fractal dimension. At last, CHNN clustering is adopted to recognize extracted features. Due to the characteristic of non-supervision, it is proper for CHNN to classify non-stationary EEG signals. The results indicate that CHNN achieves 81.9% in average classification accuracy in comparison with self-organizing map (SOM) and several popular supervised classifiers on six subjects from two data sets.

ARTIFICIAL LIFE: A NEW APPROACH TO TEXTURE CLASSIFICATION

This paper presents a novel approach to image texture classification, which involves a model of artificial organisms i.e. Artificial Crawlers (ACrawlers) and a series of evolution curves representing the features of the texture. The distributed ACrawlers locally interact with their living environment, i.e. textured regions, and each ACrawler acts according to a set of homogenous rules for isotropic motion, energy absorption and colony formation, etc. The ACrawlers evolve through natural selection, which produces the specific curves of agent evolution, habitant settlement and colony formation as well as the scale distribution of all colonies. The feasibility and effectiveness of the proposed method have been demonstrated by experiments.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.