Local Pattern Texture Research Articles

A Deep Structure of Person Re-Identification Using Multi-Level Gaussian Models

Person re-identification is being widely used in the forensic, and security and surveillance system, but person re-identification is a challenging task in real life scenario. Hence, in this work, a new feature descriptor model has been proposed using a multilayer framework of Gaussian distribution model on pixel features, which include color moments, color space values and Schmid filter responses. An image of a person usually consists of distinct body regions, usually with differentiable clothing followed by local colors and texture patterns. Thus, the image is evaluated locally by dividing the image into overlapping regions. Each region is further fragmented into a set of local Gaussians on small patches. A global Gaussian encodes, these local Gaussians for each region creating a multi-level structure. Hence, the global picture of a person is described by local level information present in it, which is often ignored. Also, we have analyzed the efficiency of earlier metric learning methods on this descriptor. The performance of the descriptor is evaluated on four public available challenging datasets and the highest accuracy achieved on these datasets are compared with similar state-of-the-arts, which demonstrate the superior performance.

Text effects transfer via distribution-aware texture synthesis

In this paper, we explore the problem of fantastic special-effects synthesis for the typography. The main challenge of this problem lies in the model diversities to illustrate varied text effects for different characters. To address this issue, we exploit the key analytics on the high regularity of the texture spatial distribution for text effects to guide the synthesis process. Specifically, we characterize the stylized patches by their normalized positions relative to the text skeleton and the optimal scales to depict their style elements. Our method first estimates these two features and derives their correlation statistically. They are then converted into soft constraints for texture transfer to accomplish adaptive multi-scale texture synthesis and to make style element distribution uniform. It allows our algorithm to produce artistic typography that well consists with both local texture patterns and the global spatial distribution in the source example. Furthermore, stroke similarities are considered to control the varieties of text effects among multiple characters in a word. Experimental results demonstrate the superiority of our distribution-aware method for various text effects over conventional style transfer methods. In addition, we validate the effectiveness of our algorithm with extensive artistic typography library generation and apply our method to a general application of special effects transfer for stroke-based graphics.

Single Classifier-Based Passive System for Source Printer Classification Using Local Texture Features

An important aspect of examining printed documents for potential forgeries and copyright infringement is the identification of source printer as it can be helpful for ascertaining the leak and detecting forged documents. This paper proposes a system for classification of source printer from scanned images of printed documents using all the printed letters simultaneously. This system uses local texture patterns based features and a single classifier for classifying all the printed letters. Letters are extracted from scanned images using connected component analysis followed by morphological filtering without the need of using an OCR. Each letter is sub-divided into a flat region and an edge region, and local tetra patterns are estimated separately for these two regions. A strategically constructed pooling technique is used to extract the final feature vectors. The proposed method has been tested on both a publicly available dataset of 10 printers and a new dataset of 18 printers scanned at a resolution of 600 dpi as well as 300 dpi printed in four different fonts. The results indicate shape independence property in the proposed method as using a single classifier it outperforms existing handcrafted feature-based methods and needs much smaller number of training pages by using all the printed letters.

Binary image steganalysis based on local texture pattern

In this paper, we propose a novel steganalytic scheme based on local texture pattern (LTP) to detect binary image steganography. We first assess how the expanded LTPs capture embedding distortions exactly. Considering curse of dimensionality when expanding LTPs, we employ Manhattan distance to measure the pixels correlation in a 5×5 sized block and select the pixels with closely correlation to remove some LTPs that are not interested. Although the stego image can maintain good visual quality, steganography scheme changes the inter-pixels correlation of binary image. Therefore we utilize totally 8192 LTPs histogram to define a 8192-dimensional steganalytic feature set. Original images and stego images are classified by ensemble classifier. Experimental results show that the proposed steganalytic method can more effectively detect state-of-the-art binary image steganography schemes compared with other steganalytic schemes.

Face recognition based on stable uniform patterns

Face recognition (FR) is one of the challenging and active research fields of image processing, computer vision and biometrics with numerous proposed systems. We present a feature extraction method named “stable uniform local pattern (SULP)”, a refined variant of ULBP operator, for robust face recognition. The SULP directly applied on gradient face images (in x and y directions) of a single image for capturing significant fundamental local texture patterns to build up a feature vector of a face image. Histogram sequences of SULP images of the two gradient images are finally concatenated to form the “stable uniform local pattern gradient (SULPG)” vector for the given image. The SULPG approach is experimented on Yale, ATT-ORL, FERET, CAS-PEAL and LFW face databases and the results are compared with the LBP model and various variants of LBP descriptor. The results indicate that the present descriptor is more powerful against a wide range of challenges, such as illumination, expression and pose variations and outperforms the state-of-the-art methods based on LBP.

A Survey on Local Textural Patterns for Facial Feature Extraction

Over the last two decades retrieving an accurate image has become a challenging task. Regardless, texture patterns address this problem by decreasing the significant gap between the actual image over the user expectation rather than other low-level features. This article represents the comprehensive survey of the recent achievements and relevant publications investigated in different directions of the textural areas in CBIR. These consist of triggered methods for image local texture feature extraction, numerical illustration and similarity measurement. In addition, challenges are discussed in comparisons of textural patterns. Retrospectively, concluded with a few recommendations based on generic survey and demand from the

Ground-based cloud classification by learning stable local binary patterns

Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set.

Adaptive Image Self-Recovery Based on Feature Extraction in the DCT Domain

Image self-recovery aims at protecting digital images from partial damage due to accidental or malicious tampering. It is done by generating a reference code that contains the information of the image and embedding the code in the image itself. This code can later be extracted to restore the tampered regions of the image. The reference code must contain sufficient information to ensure a satisfactory reconstruction while being short enough to remain invisible when embedded in the image, which requires efficient extraction and adaptive encoding of the image information. To this end, we introduce a method for extracting local features in the DCT domain, in which the locations of the three DCT peaks, i.e., the DCT coefficients with the highest magnitudes, are examined to distinguish 13 texture profiles differing in the number of edges, edge orientations, and combinations of the two. Applying this method, we propose an adaptive image self-recovery algorithm. The DCT peaks are used to identify local texture patterns, and the bit allocation is made adaptive at hierarchical levels: 1) the texture blocks get more bit allocation than the smooth blocks; 2) the blocks having texture patterns appearing more frequently in the image are encoded with more precision; and 3) in each texture block, the highest DCT peak is assigned more bits than the remaining encoded coefficients. Hence, the encoding process is not only adaptive to the levels of variations across blocks but also to the local texture patterns. The proposed algorithm generates a reference code short enough to be embedded very comfortably in a single-least-significant-bit (LSB) plane, compared to 2 ~ 3 LSB planes often found in literature. Since the reference code contains all the critical image information in a compact form, the quality of the reconstructed images is as good as those produced by significantly longer reference codes.

Insulator Detection and Defect Classification using Rotation Invariant Local Directional Pattern

Detecting power line insulator automatically and analyzing their defects are vital processes in maintaining power distribution systems. In this work, a rotation invariant texture pattern named rotation invariant local directional pattern (RI-LDP) is proposed for representing insulator image. For this at first, local directional pattern (LDP) is applied on image which can encode local texture pattern into an eight bit binary code by analyzing magnitude of edge response in eight different directions. Finally this LDP code is made robust to rotation by meticulously rearranging the generated another binary code which named as rotation invariant local directional pattern (RI-LDP). Insulator detection is carried out where this RI-LDP based histogram act as a feature vector and support vector machine (SVM) plays the role of the classifier. The detected insulator image region is further analyzed for possible defect identification. For this, an automatic extraction method of the individual insulator caps is proposed. The defect in segmented insulators is analyzed using LDP texture feature on individual cap region. We evaluated the proposed method using two sets of 493 real-world insulator images captured from a ground vehicle. The proposed insulator detector shows comparable performance to state-of-the-arts and our defect analysis method outperforms existing methods.

Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network

Cloud and snow detection has significant remote sensing applications, while they share similar low-level features due to their consistent color distributions and similar local texture patterns. Thus, accurately distinguishing cloud from snow in pixel level from satellite images is always a challenging task with traditional approaches. To solve this shortcoming, in this letter, we proposed a deep learning system to classify cloud and snow with fully convolutional neural networks in pixel level. Specifically, a specially designed fully convolutional network was introduced to learn deep patterns for cloud and snow detection from the multispectrum satellite images. Then, a multiscale prediction strategy was introduced to integrate the low-level spatial information and high-level semantic information simultaneously. Finally, a new and challenging cloud and snow data set was labeled manually to train and further evaluate the proposed method. Extensive experiments demonstrate that the proposed deep model outperforms the state-of-the-art methods greatly both in quantitative and qualitative performances.

Multiscale Adaptive Local Directional Texture Pattern for Facial Expression Recognition

Multiscale Adaptive Local Directional Texture Pattern for Facial Expression Recognition

Analysis and Controlled Synthesis of Inhomogeneous Textures

Many interesting real-world textures are inhomogeneous and/or anisotropic. An inhomogeneous texture is one where various visual properties exhibit significant changes across the texture's spatial domain. Examples include perceptible changes in surface color, lighting, local texture pattern and/or its apparent scale, and weathering effects, which may vary abruptly, or in a continuous fashion. An anisotropic texture is one where the local patterns exhibit a preferred orientation, which also may vary across the spatial domain. While many example-based texture synthesis methods can be highly effective when synthesizing uniform stationary isotropic textures, synthesizing highly non-uniform textures, or ones with spatially varying orientation, is a considerably more challenging task, which so far has remained underexplored. In this paper, we propose a new method for automatic analysis and controlled synthesis of such textures. Given an input texture exemplar, our method generates a source guidance map comprising: i a scalar progression channel that attempts to capture the low frequency spatial changes in color, lighting, and local pattern combined, and ii a direction field that captures the local dominant orientation of the texture. Having augmented the texture exemplar with this guidance map, users can exercise better control over the synthesized result by providing easily specified target guidance maps, which are used to constrain the synthesis process.

Local texture patterns for traffic sign recognition using higher order spectra

Traffic sign recognition (TSR) is considered as one of the most important modules of driver assistance system (DAS). It can be used as a decision supporting tool for driver and autonomous vehicles. Eventually, TSR is a large-scale feature learning problem and hence attracted the attention of researchers recently. The essential parameters such as huge training dataset size, recognition accuracy and computational complexity need to be considered while designing a practical TSR system. In this paper, we have used higher order spectra (HOS) coupled with texture based features to develop an efficient TSR model. These features represent the shape and content of the traffic signs clearly. Then a subspace learning method with graph embedding under linear discriminant analysis framework is used to increase the discrimination power between various traffic symbols. As a result the proposed method attained a maximum recognition accuracy of 98.89%. The proposed method is evaluated using two publicly available datasets such as, Belgium traffic sign classification (BTSC) and German traffic sign recognition benchmark (GTSRB). Our experimental results demonstrate that the proposed approach is computationally efficient and shows promising recognition accuracy.

A novel and robust rotation and scale invariant structuring elements based descriptor for pedestrian classification in infrared images

In this paper, a novel and robust rotation and scale invariant structuring elements based descriptor (RSSED) for pedestrian classification in infrared (IR) images is proposed. In addition, a segmentation method using difference of Gaussian (DoG) and horizontal intensity projection is proposed. The three major steps are moving object segmentation, feature extraction and classification of objects as pedestrian or non-pedestrian. The segmentation result is used to extract the RSSED feature descriptor. To extract features, the segmentation result is encoded using local directional pattern (LDP). This helps in the identification of local textural patterns. The LDP encoded image is further quantized adaptively to four levels. Finally the proposed RSSED is used to formalize the descriptor from the quantized image. Support vector machine is employed for classification of the moving objects in a given IR image into pedestrian and non-pedestrian classes. The segmentation results shows the robustness in extracting the moving objects. The classification results obtained from SVM classifier shows the efficacy of the proposed method.

New Fully Automated Method for Segmentation of Breast Lesions on Ultrasound Based on Texture Analysis

The study described here explored a fully automatic segmentation approach based on texture analysis for breast lesions on ultrasound images. The proposed method involves two main stages: (i) In lesion region detection, the original gray-scale image is transformed into a texture domain based on log-Gabor filters. Local texture patterns are then extracted from overlapping lattices that are further classified by a linear discriminant analysis classifier to distinguish between the “normal tissue” and “breast lesion” classes. Next, an incremental method based on the average radial derivative function reveals the region with the highest probability of being a lesion. (ii) In lesion delineation, using the detected region and the pre-processed ultrasound image, an iterative thresholding procedure based on the average radial derivative function is performed to determine the final lesion contour. The experiments are carried out on a data set of 544 breast ultrasound images (including cysts, benign solid masses and malignant lesions) acquired with three distinct ultrasound machines. In terms of the area under the receiver operating characteristic curve, the one-way analysis of variance test (α=0.05) indicates that the proposed approach significantly outperforms two published fully automatic methods (p<0.001), for which the areas under the curve are 0.91, 0.82 and 0.63, respectively. Hence, these results suggest that the log-Gabor domain improves the discrimination power of texture features to accurately segment breast lesions. In addition, the proposed approach can potentially be used for automated computer diagnosis purposes to assist physicians in detection and classification of breast masses.

A novel local mesh color texture pattern for image retrieval system

This manuscript contributes a new feature descriptor local mesh color texture pattern (LMCTP) by merging color and local spatial information for image retrieval system. The intended method combines the various features from a mixture of color spaces, the local mesh patterns of the input image. LMCTP descriptor discriminates information between the spatial color textural patterns of multi-spectral channels within the local region. It determines the relationship between surrounding neighbors of the opponent color channel. Beside this, a merged color space, I1QCb, is constructed by combining the I1 part of the I1I2I3 color space and the chromatic component images, Cb and Q, of the YCbCr and YIQ color spaces correspondingly so as to exploit the effect of joint color texture feature. The I1QCb color space, whose component of images having matching characteristics, increases the distinctive power for retrieval system. Then opponent color spaces I1Q, QCb and I1Cb are utilized for the capturing of LMCTP features. The feasibility of the proposed framework is experiments on benchmark databases such as MIT VisTex database (DB1), Corel 1000 database (DB2). Experimental study shows that the proposed pattern gives average retrieval precision of 99.8 for DB1 and 76.5 for DB2, respectively.

A Local Texture-Based Superpixel Feature Coding for Saliency Detection Combined with Global Saliency

Because saliency can be used as the prior knowledge of image content, saliency detection has been an active research area in image segmentation, object detection, image semantic understanding and other relevant image-based applications. In the case of saliency detection from cluster scenes, the salient object/region detected needs to not only be distinguished clearly from the background, but, preferably, to also be informative in terms of complete contour and local texture details to facilitate the successive processing. In this paper, a Local Texture-based Region Sparse Histogram (LTRSH) model is proposed for saliency detection from cluster scenes. This model uses a combination of local texture patterns and color distribution as well as contour information to encode the superpixels to characterize the local feature of image for region contrast computing. Combining the region contrast as computed with the global saliency probability, a full-resolution salient map, in which the salient object/region detected adheres more closely to its inherent feature, is obtained on the bases of the corresponding high-level saliency spatial distribution as well as on the pixel-level saliency enhancement. Quantitative comparisons with five state-of-the-art saliency detection methods on benchmark datasets are carried out, and the comparative results show that the method we propose improves the detection performance in terms of corresponding measurements.

Multi-model solar irradiance prediction based on automatic cloud classification

This paper proposes a framework to automatically conduct cloud classification on all-sky images and perform short-term solar irradiance prediction according to the classification results. The all-sky images are divided into blocks to deal with the mixed cloud type conditions. Local texture patterns and statistical texture features are extracted from the image blocks for cloud classification. Different cloud types with various heights, thickness, and opacity have different impact on the variation of solar irradiance. Therefore, several regression models are trained to capture the characteristics of irradiance changes under different cloud types. The current classified cloud type is used to select a corresponding prediction model. Such design substantially increases the prediction accuracy. The experimental results verify the effectiveness of the proposed framework. Both the proposed cloud classification method and irradiance prediction mechanism outperform existing works. Adding local texture patterns in the feature vector enhance the classification performance. Compared with non-block based methods, the proposed block-based method could increase the classification rate by 5%–10%. Utilizing multiple prediction models according cloud types could lower both the mean absolute error and the root mean squared error on short-term irradiance prediction.

Local Patterns of Gradients for Face Recognition

We present a novel feature extraction method named local patterns of gradients (LPOGs) for robust face recognition. LPOG uses block-wised elliptical local binary patterns (BELBP), a refined variant of ELBP, and local phase quantization (LPQ) operators directly on gradient images for capturing local texture patterns to build up a feature vector of a face image. From one input image, two directional gradient images are computed. A symmetric pair of BELBP and a LPQ operator are then separately applied upon each gradient image to generate local patterns images. Histogram sequences of local patterns images’ nonoverlapped subregions are finally concatenated to form the LPOG vector for the given image. Based on LPOG descriptor, we propose a novel face recognition system which exploits whitened principal component analysis (WPCA) for dimension reduction and weighted angle-based distance for classification. Experimental results on three large public databases (FERET, AR, and SCface) prove that LPOG WPCA system is robust against a wide range of challenges, such as illumination, expression, occlusion, pose, time-lapse variations, and low resolution. In addition, comparison with other systems shows that LPOG WPCA significantly outperforms the state-of-the-art methods. Computationally, timing benchmarks also demonstrate that our LPOG method is faster than many advanced feature extraction algorithms and can be applied in real-world applications.

QMCTLS: Quasi Monte Carlo Texture Likelihood Sampling for Despeckling of Complex Polarimetric SAR Images

Despeckling of complex polarimetric synthetic aperture radar (SAR) images is more difficult than denoising of general images due to the low signal-to-noise ratio and the complex signals. A novel stochastic polarimetric SAR despeckling technique based on quasi Monte Carlo sampling (QMCS) and region-based probabilistic similarity likelihood has been developed. The despeckling of complex polarimetric SAR images is formulated as a Bayesian least squares optimization problem, where the posterior distribution is estimated by QMCS in a nonparametric manner. The QMCS approach allows the incorporation of the statistical description of local texture pattern similarity. Experiments on two benchmark quad-pol SAR images demonstrate that the proposed QMC texture likelihood sampling (QMCTLS) filter outperforms referenced methods in terms of both noise removal and detail preservation.