•We propose the successive application of Schroedinger operator for texture recognition.•A theoretical model is provided to show how non-linear features can be identified.•The method is assessed on ...the classification of benchmark textures.•The descriptors are also applied to the identification of Brazilian plant species.•Classification accuracy outperforms state-of-the-art and classical texture features.
This work proposes a strategy for texture recognition by the successive application of the operator corresponding to the state-of-the-art Schroedinger distance transform. The idea is inspired by the recently proposed scattering networks and combines a nonlinear filter that is typically employed to provide “hand-crafted” image descriptors with a recursive scheme that can be easily adapted for an implementation similar to what is employed in convolutional neural networks. The classification performance of the proposal is assessed over well known texture databases, to know, KTHTIPS-2b, UMD and UIUC as well as in a practical problem, i.e., the identification of Brazilian plant species using images collected from their leaves. The proposed method demonstrated to be competitive with other state-of-the-art approaches and confirmed the interest in studies of composite application of nonlinear operators for texture recognition.
Image texture is an important part of many types of images, for example medical images. Texture Analysis is the technique that uses measurable features to categorize complex textures. The main ...goal is to extract discriminative features that are used in different pattern recognition applications and texture categorization. This paper investigates the extraction of most discriminative features for different texture images from the “Colored Brodatz” dataset using two types of image contrast measures, as well as using the statistical moments on five bands (red, green, blue, grey, and black). The Euclidean distance measure is used in the matching step to check the similarity degree. The proposed method was tested on 112 classes of textures. The achieved results showed that the proposed method is accurate and fast concerning classification accuracy with low computational complexity. The achieved Recognition Rate (RR) was 100%.
In this study, a novel chess based local image descriptor is presented for textural image recognition. The proposed descriptor is inspired by chess game and the main objective of it is to extract ...distinctive textural features using chess game rules. Patterns of the proposed method are created by using the movements of the knight, rook and bishop chessmen and six feature images are constructed using the proposed chess-based textural image descriptor. Therefore, this method is called as chess pattern (chess-pat) consisting of 4 phases. These four phases are block division, binary features calculation using chess patterns, histogram extraction, feature reduction with maximum pooling and classification. In the first phase, the image is divided into 5 x5 overlapping blocks. To extract the features, the proposed chess patterns are used. In the proposed chess-pat, 6 varied patterns are used based on chessmen moves and 6 feature images are created based on these patterns. Then, the histograms of these images are extracted and they are combined to create feature set of 1536 dimensions (D). In addition, maximum pooling is used to reduce this feature set with 256D and two versions of the chess-pat are obtained during the feature extraction. K-nearest neighbor (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) and are utilized for classification. To evaluate the performance of the proposed chess-pat, Outex TC 00013, Outex TC 00001, Outex TC 00000, Kylberg and 2D Hela texture datasets are used. We have obtained the best accuracy rates of 75.5%, 100.0%, 99.7%, 88.9% and 100.0% for 2D Hela, Outex TC00000, Outex TC00001, Outex TC00013, and Kylberg respectively. Also, the proposed chess-pat achieved 100.0% classification rate (perfect classification performance) for 2 datasets (Outex TC00000, Kylberg). These results confirm that our proposed chess-pat method is highly accurate.
•A novel chess based image descriptor is proposed.•The proposed method achieved high success rates for texture recognition.•A game based cognitive, lightweight and high accurate.
Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with ...the diagnosis, computer-aided diagnosis systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns, and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem. In this study, we present an improved method for training the proposed network by transferring knowledge from the similar domain of general texture classification. Six publicly available texture databases are used to pretrain networks with the proposed architecture, which are then fine-tuned on the lung tissue data. The resulting CNNs are combined in an ensemble and their fused knowledge is compressed back to a network with the original architecture. The proposed approach resulted in an absolute increase of about 2% in the performance of the proposed CNN. The results demonstrate the potential of transfer learning in the field of medical image analysis, indicate the textural nature of the problem and show that the method used for training a network can be as important as designing its architecture.
Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new modeling of the conventional LBP operator for texture classification. Named Attractive-and-Repulsive ...Center-Symmetric Local Binary Patterns (ACS-LBP and RCS-LBP), the proposed new texture descriptors preserve the advantageous characteristics of uniform LBP. Based on local attractive-and-repulsive characteristics, the proposed local texture modeling can really inherit good properties from both gradient and texture operators than the Center-Symmetric Local Binary Patterns (CS-LBP) does. Different from CS-LBP, which considers four doublets around the center pixel, the proposed methods take into account the four triplets corresponding to the vertical and horizontal directions, and the two diagonal directions by including the value of the central pixel in the modeling. In addition, Average Local Gray Level (ALGL), Average Global Gray Level (AGGL) and the median value over 3 × 3 neighborhood are introduced to capture both microstructure and macrostructure texture information. To capture the coarse and fine information of the features and thus to make ACS-LBP and RCS-LBP more robust and stable, multiscale ARCS-LBP descriptor is proposed. There is no necessity to learn texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. Extensive experiments performed on thirteen challenging representative texture databases show that the proposed operators can achieve impressive classification accuracy. Furthermore, we clearly validate the feasibility of the proposed ACS-LBP, RCS-LBP and ARCS-LBP descriptors by comparing their results with those obtained with a large number of recent state-of-the-art texture descriptors including deep features. Statistical significance of achieved accuracy improvement is demonstrated through Wilcoxon signed rank test.
•We introduce a formal definition of Attractive-and-Repulsive Binary Thresholding Functions.•Two new powerful descriptors: Attractive and Repulsive Center-Symmetric Local Binary Patterns are proposed.•Multi-scale feature is incorporated by concatenating ACS-LBP and RCS-LBP into a single vector feature.•Extensive evaluation on 13 challenging representative texture databases is performed.•72 recent state-of-the-art LBP variants as well as 3 deep learning features are evaluated.
In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference ...information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.
RGB pixel n-grams: A texture descriptor Paiva Pavón, Fátima Belén; Orué Gil, María Cristina; Vázquez Noguera, José Luis ...
Signal processing. Image communication,
October 2023, 2023-10-00, Letnik:
118
Journal Article
Recenzirano
This article proposes the “RGB Pixel N-grams” descriptor, which uses a sequence of n pixels to represent RGB color texture images. We conducted classification experiments with three different ...classifiers and five color texture image databases to evaluate the descriptor’s performance, using accuracy as the evaluation metric. These databases include various textures from different surfaces, sometimes under different lighting, scale, or rotation conditions. The proposed descriptor proved to be robust and competitive compared to other state-of-the-art descriptors, as it has better accuracy in classification results in most databases and classifiers.
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•A novel RGB texture image descriptor is proposed using the pixel n-grams approach.•Two different implementations emerged from the proposed descriptor.•The descriptor derives feature vectors with high discriminatory power.•The descriptor is compared with others showing statistically significant differences.
This work proposes a new model based on fractal descriptors for the classification of grayscale texture images. The method consists of scanning the image with a sliding box and collecting statistical ...information about the pixel distribution. Varying the box size, an estimation of the fractality of the image can be obtained at different scales, providing a more complete description of how such parameter changes in each image. The same strategy is also applied to a especial encoding of the image based on local binary patterns. Descriptors both from the original image and from the local encoding are combined to provide even more precise and robust results in image classification. A statistical model based on the theory of sliding window detection probabilities and Markov transition processes is formulated to explain the effectiveness of the method. The descriptors were tested on the identification of Brazilian plant species using scanned images of the leaf surface. The classification accuracy was also verified on three benchmark databases (KTH-TIPS2-b, UIUC and UMD). The results obtained demonstrate the power of the proposed approach in texture classification and, in particular, in the practical problem of plant species identification.
•Texture descriptors using a sliding box counting approach.•Theory based on fractal geometry and sliding detection probabilities.•Employed in the classification of gray level texture images.•Application to the identification of Brazilian plant species.•Better classification accuracy both on benchmark and practical tasks.
•We propose a novel texture descriptor to handle color images.•A multi-channel color order pattern is explored to jointly encode inter-channel features.•A discriminative color gradient channel is ...introduced to complement the RGB channels.•Local color differences are decomposed to encode the spatial relationships of color orders.•The proposed method achieves the state-of-the-art results for color texture classification.
In this paper, we propose a novel descriptor called spatially weighted order binary pattern (SWOBP) for color texture classification. The SWOBP descriptor not only encodes color order information in different channels but also encodes color order relationships in the spatial domain. To achieve these goals, we introduce a color gradient channel to complement the traditional color channels and explore a multi-channel color order pattern to jointly encode inter-channel features. Furthermore, we decompose local color differences into spatially weighted binary templates and use them to encode color order information in a local neighborhood. Finally, we aggregate all the encoded features into image histograms as texture descriptor. Experiments on five benchmark databases demonstrate that the proposed SWOBP descriptor achieves the state-of-the-art performance for color texture classification.
Color texture classification has recently attracted significant attention due to its multiple applications. The color texture images depend on the texture surface and its albedo, the illumination, ...the camera and its viewing position. A key problem to get an acceptable performance is the ambient illumination, which can vary the perceived structures in the surface. Given a color texture classification problem, it would be desirable to know which is the best approach to solve the problem making the minimal assumptions about the illumination conditions. The present work does an exhaustive evaluation of the state-of-the-art color texture classification methods, considering 5 different color spaces, 12 normalization methods to achieve illumination invariances, 19 texture feature vectors and 23 pure color feature vectors. Our experiments allow to conclude that parallel approaches are better than integrative approaches for color texture classification achieving the first positions in the Friedman ranking. Multiresolution Local Binary Patterns (MLBP) are the best intensity texture features, followed by wavelet and Gabor filters combined with luminance–chrominance color spaces (Lab and Lab2000HL), and for pure color classification the best are First Order Statistics (FOS) calculated in RGB color space. For intensity texture features, the learning methods work better on the four smallest datasets, although they could not be tested in other four bigger datasets due to its huge computational cost, nor with color texture classification. Normalization and color spaces slightly increase the average accuracy of color texture classification, although the differences achieved using normalization are not statistically significant in a paired T-Test. Lab2000HL and RGB are the best color spaces, but the former is the slowest one. Regarding elapsed time, the best vector features MLBP for intensity texture, Daub4 (Daubechies filters using mean and variance statistics) for color texture and FOS, for pure color are nearly the fastest or are in the middle interval of all the tested methods.
•Study the influence of normalization and color space to color texture classification.•More than 2000 texture and color feature vectors are tested.•Color texture databases used: CUReT, Outex, Vistex, USPTex and ALOT.•Compare parallel and integrative color texture approaches on all datasets.
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