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Journal articles on the topic 'Brodatz texture'
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1
Abdelmounaime, Safia, and He Dong-Chen. "New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis." ISRN Machine Vision 2013 (February 24, 2013): 1–14. http://dx.doi.org/10.1155/2013/876386.
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Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.
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Murala, Subrahmanyam, R. P. Maheshwari, and R. Balasubramanian. "Multiresolution LBP Correlogram for Texture Image Indexing and Retrieval." Advanced Materials Research 403-408 (November 2011): 908–14. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.908.
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A new image indexing and retrieval algorithm known as local binary pattern (LBP) correlogram is presented in this paper. LBP histogram captures only the patterns distribution in a texture while the spatial correlation between the pair of patterns is gathered by LBP correlogram. Multi-resolution texture decomposition and color correlation has been efficiently used in the proposed method where multi-resolution texture images are computed using Gaussian filter for collection of LBPs from these particular textures. Eventually, feature vectors are constructed by making into play the auto-correlation that exists between binary patterns. The retrieval results of the proposed method are examined on different texture image databases viz Brodatz database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and small set of rotated Brodatz database (DB4), and shows a major improvement in terms of average retrieval rate as when weighed against with LBP histogram and some existing transform domain technique.
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Hemalatha, S., and S. Margret Anouncia. "A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection." International Journal of Ambient Computing and Intelligence 7, no. 2 (2016): 93–113. http://dx.doi.org/10.4018/ijaci.2016070105.
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This paper is dedicated to the modelling of textured images influenced by fractional derivatives for texture detection. As most of the images contain textures, texture analysis becomes the most important for image understanding and it is a key solution for many computer vision applications. Hence, texture must be suitably detected and represented. Nevertheless, existing texture detection algorithms consider texture as a unique feature from edges. The proposed model explores a novel way of developing texture detection algorithm by mimicking edge detection algorithms. The method assumes that texture feature is analogous to edges and thus, the time complexity is reduced significantly. The model proposed in this work is based on Gaussian kernel smoothing, Fractional partial derivatives and a statistical approach. It is justified to be robust to noisy images and possesses statistical interpretation. The model is validated by the classification experiments on different types of textured images from Brodatz album. It achieves higher classification accuracy than the existing methods.
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MUNEESWARAN, K., L. GANESAN, S. ARUMUGAM, and P. HARINARAYAN. "A NOVEL APPROACH COMBINING GABOR WAVELET AND MOMENTS FOR TEXTURE SEGMENTATION." International Journal of Wavelets, Multiresolution and Information Processing 03, no. 04 (2005): 559–72. http://dx.doi.org/10.1142/s0219691305001020.
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In this work, an effective method has been proposed for texture segmentation, which incorporates the best features of filter bank and statistical approaches. This technique combines the features of Gabor wavelets (filter based) and General Moments (statistical) approaches. The method has been successfully tested for various textures from Brodatz texture collection. The relative performance of this method against the conventional approaches has been analyzed using Fisher Criterion.
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YANG, GUAN, GUO-CAN FENG, ZHI-HONG LUO, and ZHI-YONG LIU. "TEXTURE ANALYSIS USING GAUSSIAN GRAPHICAL MODELS." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 02 (2012): 1250015. http://dx.doi.org/10.1142/s0219691312500154.
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Texture classification is a challenging and important problem in image analysis. graphical models (GM) are promising tools for texture analysis. In this paper, we address the problem of learning the structure of Gaussian graphical models (GGM) for texture models. GGM can be considered as regression problems due to the connection between the local Markov properties and conditional regression of a Gaussian random variable. We utilize L1-penalty regularization technique for appropriate neighborhood selection and parameter estimation simultaneously. The proposed algorithms are applied in texture synthesis and classification. Experimental results on Brodatz textures demonstrate that the proposed algorithms have good performance and prospects.
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ROOMI, S. MOHAMED MANSOOR, R. RAJA, and D. KALAIYARASI. "COMPUTING IMAGE TEXTURE BY ISOPATTERN." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 05 (2014): 1454003. http://dx.doi.org/10.1142/s0218001414540032.
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Texture is an important feature that aids in identifying objects of interest or region of interest irrespective of the source of the image. In this paper, a novel and simple isopattern-based texture feature is introduced. Spatial gray scale dependencies represented by bit plane is analyzed for specific patterns and are accumulated in bins. These are scaled by half-normal weighting function to provide isopattern texture feature. The ability of this texture feature in capturing textural variations of the images despite the presence of illumination, scale and rotation is demonstrated by conducting texture analysis on Brodatz, OuTex texture datasets and its classification accuracy on Kylberg dataset. The results of these two experimentation indicate that the proposed textural feature picks variation in texture significantly and has a better texture classification accuracy of 98.26% when compared with the state-of-the-art features like Gabor, GLCM and LBP.
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Soares, Lucas de Assis, Klaus Fabian Côco, Patrick Marques Ciarelli, and Evandro Ottoni Teatini Salles. "A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification." Sensors 20, no. 18 (2020): 5432. http://dx.doi.org/10.3390/s20185432.
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Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.
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Materka, Andrzej, and Michał Strzelecki. "On The Effect Of Image Brightness And Contrast Nonuniformity On Statistical Texture Parameters." Foundations of Computing and Decision Sciences 40, no. 3 (2015): 163–85. http://dx.doi.org/10.1515/fcds-2015-0011.
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Abstract Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture descriptors also depend on local average image intensity and/or contrast. This variations, known as image nonuniformity (inhomogeneity) artefacts often occur, e.g. in MRI. Their presence may lead to errors in tissue description. This unwanted effect is explained in this paper using statistical texture descriptors applied for MRI slices of a normal and fibrotic liver. To reduce the errors, correction of image spatial nonuniformity prior to texture analysis is performed. The issue of sensitivity of popular texture parameters to image nonuniformities is discussed. It is illustrated by classification examples of natural Brodatz textures, digitally modified to account for inhomogeneities – modeled as smooth variations of image intensity and contrast. A set of texture features is identified which represent certain immunity to image inhomogeneities.
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Suruliandi, A., A. Sinduja, and S. P. Raja. "Texture classification using the rotational-invariant local symmetric tetra pattern." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 04 (2019): 1950027. http://dx.doi.org/10.1142/s0219691319500279.
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Feature extraction plays a key role in pattern recognition problems. The texture feature is an important feature which helps to describe an image with textural information. A new texture descriptor, the Local Symmetric Tetra Pattern (LSTP), is proposed in this work. This descriptor is developed for the local description of an image. It considers not only the surrounding eight neighbors, but also the eight pixels at the next level to describe the texture efficiently. For every pixel, the maximum edge value, the number of negative sign bits and the number of positive sign bits for each degree of symmetry are computed. Image classification is experimented using the Original Brodatz, Outex and Kylberg Texture Dataset v.1.0 databases. The investigation results are compared with existing method which shows promising achievement of the proposed techniques in terms of their evaluation measures. It is also found that the proposed texture descriptor is rotationally invariant.
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Vijay Kumar, Palnati, Pullela S. V. V. S. R. Kumar, Nakkella Madhuri, and M. Uma Devi. "Stone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1152. http://dx.doi.org/10.11591/ijece.v6i3.9233.
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Texture classification is widely used in understanding the visual patterns and has wide range of applications. The present paper derived a novel approach to classify the stone textures based on the patterns occurrence on each sub window. The present approach identifies overlapped nine 5 bit T-patterns (O5TP) on each 5×5 sub window stone image. Based the number of occurrence of T-patterns count the present paper classify the stone images into any of the four classes i.e. brick, granite, marble and mosaic stone images. The novelty of the present approach is that no standard classification algorithm is used for the classification of stone images. The proposed method is experimented on Mayang texture images, Brodatz textures, Paul Bourke color images, VisTex database, Google color stone texture images and also original photo images taken by digital camera. The outcome of the results indicates that the proposed approach percentage of grouping performance is higher to that of many existing approaches.
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Vijay Kumar, Palnati, Pullela S. V. V. S. R. Kumar, Nakkella Madhuri, and M. Uma Devi. "Stone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1152. http://dx.doi.org/10.11591/ijece.v6i3.pp1152-1160.
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Texture classification is widely used in understanding the visual patterns and has wide range of applications. The present paper derived a novel approach to classify the stone textures based on the patterns occurrence on each sub window. The present approach identifies overlapped nine 5 bit T-patterns (O5TP) on each 5×5 sub window stone image. Based the number of occurrence of T-patterns count the present paper classify the stone images into any of the four classes i.e. brick, granite, marble and mosaic stone images. The novelty of the present approach is that no standard classification algorithm is used for the classification of stone images. The proposed method is experimented on Mayang texture images, Brodatz textures, Paul Bourke color images, VisTex database, Google color stone texture images and also original photo images taken by digital camera. The outcome of the results indicates that the proposed approach percentage of grouping performance is higher to that of many existing approaches.
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Wang, Hai Feng, Kun Zhang, and Hong E. Ren. "A Gabor Wavelet Transformation-Based Texture Images Classification Algorithm." Advanced Materials Research 811 (September 2013): 430–34. http://dx.doi.org/10.4028/www.scientific.net/amr.811.430.
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In this paper, we introduce a texture image classification algorithm based on Gabor wavelet transform. Using Gabor wavelet transform, image is decomposed into sub-bands images in multiresolution and multi-direction, and we extract texture feature from all sub-bands images. Then the algorithm groups feature image into clusters by the k near neighbor algorithm. The experimental results on dataset Brodatz showed that the proposed algorithm can achieve an ideal accuracy rate and excellent classification effect.
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Alharan, Abbas F. H., Hayder K. Fatlawi, and Nabeel Salih Ali. "A cluster-based feature selection method for image texture classification." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (2019): 1433. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1433-1442.
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<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>
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Volkova, Natalya P., and Viktor N. Krylov. "HYBRID TEXTURE IDENTIFICATION METHOD." Herald of Advanced Information Technology 4, no. 2 (2021): 123–34. http://dx.doi.org/10.15276/hait.02.2021.2.
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The importance of the modeling mode in systems of computer visual pattern recognition is shown. The purpose of the mode is to determine the types of textures that are present on the images processed in intelligent diagnostic systems. Images processed in technical diagnostic systems contain texture regions, which can be represented by different types of textures - spectral, statistical and spectral-statistical. Texture identification methods, such as, statistical, spectral, expert, multifractal, which are used to identify and analyze texture images, have been analyzed. To determine texture regions on images that are of a combined spectral-statistical nature, a hybrid texture identification method has been developed which makes it possible to take into account the local characteristics of the texture based on multifractal indicators characterizing the non-stationarity and impulsite of the data and the sign of the spectral texture. The stages of the developed hybrid texture identification method are: preprocessing; formation of the primary features vector; formation of the secondary features vector. The formation of the primary features vector is performed for the selected rectangular fragment of the image, in which the multifractal features and the spectral texture feature are calculated. To reduce the feature space at the stage of formation of the secondary identification vector, the principal component method was used. An experimental study of the developed hybrid texture identification method textures on model images of spectral, statistical, spectralstatistical textures has been carried out. The results of the study showed that the developed method made it possible to increase the probability of correct determination of the region of the combined spectral-statistical texture. The developed identification method was tested on images from Brodatz album of textures and images of wear zones of cutting tools, which are processed in intelligent systems of technical diagnostics. The probability of correctly identifying areas of spectral-statistical texture in the images of wear zones of cutting tools averaged 0.9, which is sufficient for the needs of practice
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Xu, Guang Zhu, Bang Jun Lei, Jing Jing Zhao, and Chun Lin Li. "Using LBP to Improve PCNN Performance for Texture Image Retrieval." Applied Mechanics and Materials 530-531 (February 2014): 480–88. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.480.
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Local binary pattern (LBP) spectrum is a powerful feature for texture image, which is invariant to local illumination changes. Pulse coupled neural network (PCNN) is a biologically inspired algorithm, which is well suited for image processing and can generate rotation, scale, translation invariant image signature. This paper proposed an image retrieval tool named LBP-PCNN which combined the advantages of LBP and PCNN. First, images are mapped into local structural domain with rotation invariant LBP. Then, the simplified PCNN was adapted to extract the image signature in structural domain. At last, texture image retrieval was completed by measuring the signature similarity of input sample texture image and texture database. Experimental results on Brodatz texture gallery show that LBP-PCNN overbears LBP and PCNN in texture image retrieval and has potential applications.
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P., Ramesh, and V. Mathivanan. "Texture Classification Based on Empirical Wavelet Transform Using LBP Features." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 3 (2017): 623. http://dx.doi.org/10.11591/ijeecs.v8.i3.pp623-626.
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<p>Automatic inspection systems become more importance for industries with high productive plans especially in texture industry. A novel approach to Local Binary Pattern (LBP) feature for texture classification is proposed in this system. At the first, the proposed Empirical Wavelet Transform (EWT) based texture classification is tested on gray scale and color images by using Brodatz texture images. The gray scale and color image is decomposed by EWT at 2 and 3 level of decomposition. LBP features are calculated for each empirical transformed image. Extracted features are given as input to the classification stage. K-NN classifier is used for classification stage. The result of the proposed system gives satisfactory classification accuracy of over 98% for all types of images.</p>
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VYAS, VIBHA S., and PRITI P. REGE. "GEOMETRIC TRANSFORM INVARIANT TEXTURE ANALYSIS WITH MODIFIED CHEBYSHEV MOMENTS BASED ALGORITHM." International Journal of Image and Graphics 09, no. 04 (2009): 559–74. http://dx.doi.org/10.1142/s0219467809003587.
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Texture based Geometric invariance, which comprises of rotation scale and translation (RST) invariant is finding application in various areas including industrial inspection, estimation of object range and orientation, shape analysis, satellite imaging, and medical diagnosis. Moments based techniques, apart from being computationally simple as compared to other RST invariant texture operators, are also robust in presence of noise. Zernike moments (ZM) based techniques are one of the well-established methods used for texture identification. As ZM are continuous moments, when discretization is done for implementation, errors are introduced. Error, calculated as difference between theoretically computed values and simulated values is proved to be prominent for fine textures. Therefore, a novel approach to detect RST invariant textures present in image is presented in this paper. This approach calculates discrete Chebyshev moments (CM) of log-polar transformed images to achieve rotation and scale invariance. The image is made translation invariant by shifting it to its centroid. The data is collected as samples from Brodatz and Vistex data sets. Zernike moments and its modifications, along with proposed scheme are applied to the same and Performance evaluation apart from RST invariance is noise sensitivity and redundancy. The performance is also compared with circular Mellin Feature extractors.
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Chen, Ying, and Feng Yu Yang. "Analysis of Image Texture Features Based on Gray Level Co-Occurrence Matrix." Applied Mechanics and Materials 204-208 (October 2012): 4746–50. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4746.
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Gray level co-occurrence matrix (GLCM) is a second-order statistical measure of image grayscale which reflects the comprehensive information of image grayscale in the direction, local neighborhood and magnitude of changes. Firstly, we analyze and reveal the generation process of gray level co-occurrence matrix from horizontal, vertical and principal and secondary diagonal directions. Secondly, we use Brodatz texture images as samples, and analyze the relationship between non-zero elements of gray level co-occurrence matrix in changes of both direction and distances of each pixels pair by. Finally, we explain its function of the analysis process of texture. This paper can provided certain referential significance in the application of using gray level co-occurrence matrix at quality evaluation of texture image.
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Ge, Jing, and Xin Wu Chen. "Contourlet-1.3 Texture Retrieval Algorithm by Sub-Bands Energy and Consistency." Applied Mechanics and Materials 220-223 (November 2012): 2684–87. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2684.
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Contourlet-1.3 transform has fewer artifacts than original contourlet transform proposed by Do in 2002; it can extract image texture information more efficiently and has been studied for image de-noising, enhancement, and retrieval situations. Focus on improving the retrieval rate of contourlet-1.3 transform retrieval system, a new contourlet-1.3 texture retrieval algorithm was proposed in this paper. The feature vector of this system was a combination of sub-band energy and consistency and the similarity measure function used here was Canberra distance. Experimental results on 109 texture images coming from Brodatz album show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and system structure.
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Chen, Xin Wu, and Li Wei Liu. "Contourlet-1.3 Texture Retrieval Using Absolute Mean Energy and Kurtosis Features." Applied Mechanics and Materials 48-49 (February 2011): 327–30. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.327.
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To improve the texture image retrieval rate of contourlet texture image retrieval system, a contourlet-1.3 transform based texture image retrieval system was proposed. In the system, the contourlet transform was contourlet-1.3, a new version of the original contourlet, sub-bands absolute mean energy and kurtosis in each contourlet-1.3 sub-band were cascaded to form feature vectors, and the similarity metric was Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean energy and kurtosis can lead to a higher retrieval rate than the combination of standard deviation and absolute mean energy which is most commonly used today under same dimension of feature vectors. Contourlet-1.3 transform based image retrieval system is superior to those of the original contourlet, non-subsampled contourlet and contourlet-2.3 systems under same system structure with same dimension of feature vectors, retrieval time and memory needed.
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Dhingra, Shefali, and Poonam Bansal. "Employing Divergent Machine Learning Classifiers to Upgrade the Preciseness of Image Retrieval Systems." Cybernetics and Information Technologies 20, no. 3 (2020): 75–85. http://dx.doi.org/10.2478/cait-2020-0029.
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AbstractContent Based Image Retrieval (CBIR) system is an efficient search engine which has the potentiality of retrieving the images from huge repositories by extracting the visual features. It includes color, texture and shape. Texture is the most eminent feature among all. This investigation focuses upon the classification complications that crop up in case of big datasets. In this, texture techniques are explored with machine learning algorithms in order to increase the retrieval efficiency. We have tested our system on three texture techniques using various classifiers which are Support vector machine, K-Nearest Neighbor (KNN), Naïve Bayes and Decision Tree (DT). Variant evaluation metrics precision, recall, false alarm rate, accuracy etc. are figured out to measure the competence of the designed CBIR system on two benchmark datasets, i.e. Wang and Brodatz. Result shows that with both these datasets the KNN and DT classifier hand over superior results as compared to others.
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Chen, Xin Wu, and Zhan Qing Ma. "Material Texture Retrieval Using Contourlet-2.3 and Three Statistical Features." Advanced Materials Research 233-235 (May 2011): 2495–98. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.2495.
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To improve the retrieval rate of contourlet transform texture retrieval system, a contourlet-2.3 transform based retrieval system was proposed. Six different features, including mean, standard deviation, absolute mean energy, L2 energy, skewness and kurtosis contributions to retrieval rates were examined. Based on the single feature ability in retrieval system, a contourlet-2.3 retrieval system was proposed. The feature vectors were constructed by cascading the standard deviation, absolute mean energy and kurtosis of each sub-band contourlet coefficients and the similarity measure used here is Canberra distance. Experimental results on 109 brodatz texture images show that the new retrieval algorithm can lead to a higher retrieval rate than several contourlet transform retrieval systems including the original contourlet transform, non-subsampled contourlet transform under the same structure.
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Krishnamoorthi, R., and S. Sathiya Devi. "Rotation Invariant Texture Image Retrieval with Orthogonal Polynomials Model." International Journal of Computer Vision and Image Processing 1, no. 4 (2011): 27–49. http://dx.doi.org/10.4018/ijcvip.2011100103.
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The exponential growth of digital image data has created a great demand for effective and efficient scheme and tools for browsing, indexing and retrieving images from a collection of large image databases. To address such a demand, this paper proposes a new content based image retrieval technique with orthogonal polynomials model. The proposed model extracts texture features that represent the dominant directions, gray level variations and frequency spectrum of the image under analysis and the resultant texture feature vector becomes rotation and scale invariant. A new distance measure in the frequency domain called Deansat is proposed as a similarity measure that uses the proposed feature vector for efficient image retrieval. The efficiency of the proposed retrieval technique is experimented with the standard Brodatz, USC-SIPI and VisTex databases and is compared with Discrete Cosine Transform (DCT), Tree Structured Wavelet Transform (TWT) and Gabor filter based retrieval schemes. The experimental results reveal that the proposed method outperforms well with less computational cost.
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Rana, Bharti, Akanksha Juneja, and Ramesh Kumar Agrawal. "Relevant Feature Subset Selection from Ensemble of Multiple Feature Extraction Methods for Texture Classification." International Journal of Computer Vision and Image Processing 5, no. 1 (2015): 48–65. http://dx.doi.org/10.4018/ijcvip.2015010103.
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Performance of texture classification for a given set of texture patterns depends on the choice of feature extraction technique. Integration of features from various feature extraction methods not only eliminates risk of method selection but also brings benefits from the participating methods which play complimentary role among themselves to represent underlying texture pattern. However, it comes at the cost of a large feature vector which may contain redundant features. The presence of such redundant features leads to high computation time, memory requirement and may deteriorate the performance of the classifier. In this research workMonirst phase, a pool of texture features is constructed by integrating features from seven well known feature extraction methods. In the second phase, a few popular feature subset selection techniques are investigated to determine a minimal subset of relevant features from this pool of features. In order to check the efficacy of the proposed approach, performance is evaluated on publically available Brodatz dataset, in terms of classification error. Experimental results demonstrate substantial improvement in classification performance over existing feature extraction techniques. Furthermore, ranking and statistical test also strengthen the results.
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BASHAR, M. K., and N. OHNISHI. "WAVELET-BASED SALIENT ENERGY POINTS FOR UNSUPERVISED TEXTURE SEGMENTATION." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 03 (2005): 429–58. http://dx.doi.org/10.1142/s0218001405004113.
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Despite extensive research on image texture analysis, it is still problematic to characterize and segment texture images especially in the presence of complex patterns. Upon tremendous advancement of the internet and the digital technology, there is also a need for the development of simple but efficient algorithms, which can be adaptable to real-time systems. In this study, we propose such an approach based on multiresolution discrete wavelet transform (DWT). After the transform, we compute salient energy points from each directional sub-band (LH, HL, and HH) in the form of binary image by thresholding intermittency indices of wavelet coefficients. We then propose and extract two new texture features namely Salient Point Density (SPD) and Salient Point Distribution Nonuniformity (SPDN) based on the number and the distribution of salient pixels in the local neighborhood of every pixel of the multiscale binary images. We thus obtain a set of feature images, which are subsequently applied to the popular K-means algorithm for the unsupervised segmentation of texture images. Though the above representation appear simple and infrequent in the literature, it proves useful in the context of texture segmentation. Experimental results with the standard texture (Brodatz) and natural images demonstrate the robustness and potentiality of the proposed approach.
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Chen, Ying, and Feng Yu Yang. "Research on Characteristic Properties of Gray Level Co-Occurrence Matrix." Applied Mechanics and Materials 204-208 (October 2012): 4755–59. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4755.
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Gray level co-occurrence matrix (GLCM) is a second-order statistical measurement. In order to understand the characterization degree of GLCM’s different feature properties, we use images of Brodatz texture images as experimental samples, analyze the change process of feature properties in horizontal, vertical and principal and secondary diagonal directions under the situation of some elements’ dynamic changes such as distance of pixels pair, size of moving window and gray level quantization,. By analyzing the experimental results, this paper can provided certain referential significance in how to select feature properties reasonable in the application of image retrieval and classification and identification which are based on using GLCM as feature.
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Navarro and Perez. "Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing." Applied Sciences 9, no. 15 (2019): 3130. http://dx.doi.org/10.3390/app9153130.
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Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.
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Liu, Yu Xi, and Xin Wu Chen. "Semi-Subsampled Contourlet Retrieval Algorithm Using Three Statistical Features." Advanced Materials Research 433-440 (January 2012): 3117–23. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3117.
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In order to improve the retrieval rate of contourlet transform retrieval system, a semi-subsampled contourlet transform based texture image retrieval system was proposed. In the system, the contourlet transform was constructed by non-subsampled Laplacian pyramid cascaded by critical subsampled directional filter banks, sub-bands standard deviation, absolute mean energy and kurtosis in semi-subsampled contourlet domain are cascaded to form feature vectors, and the similarity metric is Canberra distance. Experimental results on 109 brodatz texture images show that using the three cascaded features can lead to a higher retrieval rate than the combination of standard deviation and absolute mean which is most commonly used today under same dimension of feature vectors. Semi-subsampled contourlet transform based image retrieval system is superior to those of the original contourlet transform, non-subsampled contourlet system under the same system structure with same length of feature vectors, retrieval time and memory needed, decomposition structure parameters can also make significant effects on retrieval rates, especially scale number.
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Youness, Chawki, El Asnaoui Khalid, Ouanan Mohammed, and Aksasse Brahim. "New Method of Content Based Image Retrieval based on 2-D ESPRIT Method and the Gabor Filters." TELKOMNIKA Indonesian Journal of Electrical Engineering 15, no. 2 (2015): 313. http://dx.doi.org/10.11591/tijee.v15i2.1544.
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We propose, in this paper, a new method for Content Based Image Retrieval (CBIR) by exploiting the digital image content. Our method is based on the representation of the digital image content by a characteristics vector of the indexed image. Indeed, we have exploited the image texture to extract its characteristics and for constructing a new descriptor vector by combining the Bidimensional High Resolution Spectral Analysis 2-D ESPRIT (Estimation of Signal Parameters via Rotationnal Invariance Techniques) method and Gabor filter. To evaluate the performance, we have tested our approach on Brodatz image database. The results show that the representation of the digital image content appears significant in research of imaging information.
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Groner, R., A. von Mühlenen, and M. Groner. "Top-Down versus Bottom-up Control of Saccades in Texture Perception." Perception 26, no. 1_suppl (1997): 162. http://dx.doi.org/10.1068/v970141.
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An experiment was conducted to examine the influence of luminance, contrast, and spatial frequency content on saccadic eye movements. 112 pictures of natural textures from Brodatz were low-pass filtered (0.04 – 0.76 cycles deg−1) and high-pass filtered (1.91 – 19.56 cycles deg−1) and varied in luminance (low and high) and contrast (low and high), resulting in eight images per texture. Circular clippings of the central parts of the images (approximately 15% of the whole image) were used as stimuli. In the condition of bottom - up processing, the eight stimuli derived from one texture were presented for 1500 ms in a circular arrangement around the fixation cross. They were followed by a briefly presented target stimulus in the centre, which in half the trials was identical to one of the eight test stimuli. Participants had to decide whether the target stimulus was identical to any of the preceding stimuli. During a trial, their eye movements were recorded by means of a Dual-Purkinje-Image eye tracker. In the top - down condition, the target stimulus was presented in each trial prior to the display of the test stimulus. It was assumed that the priming with a target produced a top - down processing of the test stimuli. The latency and landing site of the first saccade were computed and compared between the top - down and bottom - up conditions. It is hypothesised that stimulus characteristics (luminance, contrast, and spatial frequency) play a more prominent role in bottom - up processing, while top - down processing is adjusted to the particular characteristics of the prime.
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Raju, U. S. N., K. Suresh Kumar, Pulkesh Haran, Ramya Sree Boppana, and Niraj Kumar. "Content-based image retrieval using local texture features in distributed environment." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 01 (2019): 1941001. http://dx.doi.org/10.1142/s0219691319410017.
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In this paper, we propose novel content-based image retrieval (CBIR) algorithms using Local Octa Patterns (LOtP), Local Hexadeca Patterns (LHdP) and Direction Encoded Local Binary Pattern (DELBP). LOtP and LHdP encode the relationship between center pixel and its neighbors based on the pixels’ direction obtained by considering the horizontal, vertical and diagonal pixels for derivative calculations. In DELBP, direction of a referenced pixel is determined by considering every neighboring pixel for derivative calculations which results in 256 directions. For this resultant direction encoded image, we have obtained LBP which is considered as feature vector. The proposed method’s performance is compared to that of Local Tetra Patterns (LTrP) using benchmark image databases viz., Corel 1000 (DB1) and Brodatz textures (DB2). Performance analysis shows that LOtP improves the average precision from 59.31% to 64.36% on DB1, and from 83.24% to 85.95% on DB2, LHdP improves it to 65.82% on DB1 and to 87.49% on DB2 and DELBP improves it to 60.35% on DB1 and to 86.12% on DB2 as compared to that of LTrP. Also, DELBP reduces the feature vector length by 66.62% as compared to that of LTrP. To reduce the retrieval time, the proposed algorithms are implemented on a Hadoop cluster consisting of 116 nodes and tested using Corel 10K (DB3), Mirflickr 100,000 (DB4) and ImageNet 511,380 (DB5) databases.
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Paskaš, Milorad P., Irini S. Reljin, and Branimir D. Reljin. "Multifractal Framework Based on Blanket Method." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/894546.
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This paper proposes two local multifractal measures motivated by blanket method for calculation of fractal dimension. They cover both fractal approaches familiar in image processing. The first two measures (proposed Methods 1 and 3) support model of image with embedded dimension three, while the other supports model of image embedded in space of dimension three (proposed Method 2). While the classical blanket method provides only one value for an image (fractal dimension) multifractal spectrum obtained by any of the proposed measures gives a whole range of dimensional values. This means that proposed multifractal blanket model generalizes classical (monofractal) blanket method and other versions of this monofractal approach implemented locally. Proposed measures are validated on Brodatz image database through texture classification. All proposed methods give similar classification results, while average computation time of Method 3 is substantially longer.
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Li, Xiang Ying, Rui Xue, Xin Wu Chen, and Wei Luo. "Contourlet-S Retrieval Algorithm Using Absolute Mean Energy and Kurtosis Features." Applied Mechanics and Materials 197 (September 2012): 473–76. http://dx.doi.org/10.4028/www.scientific.net/amm.197.473.
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Contourlet transform has better performance in directional information representation than wavelet transform and has been studied by many researchers in retrieval systems and has been shown that it is superior to wavelet ones at retrieval rate. In order to improve the retrieval rate further, a contourlet-S transform based texture image retrieval system was proposed in this paper. In the system, the contourlet transform was constructed by non-subsampled Laplacian pyramid cascaded by critical subsampled directional filter banks, sub-bands absolute mean energy and kurtosis in contourlet-S domain are cascaded to form feature vectors, and the similarity metric is Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean and kurtosis can lead to a higher retrieval rate than the combination of standard deviation and absolute mean which is most commonly used today under same dimension of feature vectors. contourlet-S transform based image retrieval system is superior to those of the original contourlet transform, non-subsampled contourlet system under the same system structure with same length of feature vectors, retrieval time and memory needed, contourlet-S decomposition structure parameters can make significant effects on retrieval rates, especially scale number.
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Chen, Xin Wu, Zhan Qing Ma, and Li Wei Liu. "Wavelet-Contourlet Retrieval Using Energy and Kurtosis Features." Advanced Materials Research 201-203 (February 2011): 2330–33. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.2330.
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To improve the retrieval rate of contourlet transform retrieval system and reduce the redundancy of contourlet which cost two much time in building feature vector database, a new wavelet-contourlet transform retrieval system was proposed. Six different features, including mean, standard deviation, absolute mean energy, L2 energy, skewness and kurotis contributions to retrieval rates were examined. Based on the single feature ability in retrieval system, a new contourlet retrieval system was proposed. The feature vectors were constructed by cascading the absolute mean energy and kurtosis of each sub-band contourlet coefficients and the similarity measure used here is Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean and kurtosis can lead to a higher retrieval rate than several contourlet transform retrieval systems which utilize the combination feature of standard deviation and absolute mean energy most commonly used today under same dimension of feature vectors.
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I.Jeena Jacob, Dr, Dr K.G.Srinivasagan, Dr S. Gomathi, Ms Joyce Beryl Princess, Ms P.Betty, and Mr P.Ebby Darney. "Tri-Chrominance Texture Pattern: A New Feature Descriptor." International Journal of Engineering & Technology 7, no. 2.22 (2018): 15. http://dx.doi.org/10.14419/ijet.v7i2.22.11801.
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Feature extraction plays a vital role in the information management system. This paper proposes Tri-Chrominance Texture Pattern (TCTP), a feature descriptor for extracting the features from images. This pattern helps to extract the inter-channel chrominance relationship, along with texture information of the image. The analysis were done in a natural image dataset, Corel database (DB1), pure colored texture database, Colored Brodaz Texture database (DB2) and a biometric dataset, Indian Face Image database (DB3). The proposed work outperforms the existing works in all the datasets. The analysis on DB1 shows significant improvement over the previous works like Local Binary Pattern (LBP) (78.64%/57.35%), Local Tetra Pattern (LTrP) (79.84%/56.8%) and Local Oppugnant Color Texture Pattern (LOCTP) (82.64%/58%) as 83.25%/58.2% in terms of Average Precision/ Average Recall. The analysis made in the Colored Brodaz database (DB1) shows the result of TCTP as improved from LBP (91.75%/75.18%), LTrP (91.64%/76%) and LOCTP (99.21%/89.38%) to (99.8%/93.47%). The Average Recognition Rate (ARR) of face recognition in DB3 database using the proposed work shows considerable improvement from LBP (78.2%), LTrP (91.9%) and LOCTP (89.1%) as 88.5%. The computational complexity of the proposed work is much lesser than LTrP and LOCTP.
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Li, Xiaojun, Yide Ma, Zhaobin Wang, and Wenrui Yu. "Geometry-Invariant Texture Retrieval Using a Dual-Output Pulse-Coupled Neural Network." Neural Computation 24, no. 1 (2012): 194–216. http://dx.doi.org/10.1162/neco_a_00194.
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This letter proposes a novel dual-output pulse coupled neural network model (DPCNN). The new model is applied to obtain a more stable texture description in the face of the geometric transformation. Time series, which are computed from output binary images of DPCNN, are employed as translation-, rotation-, scale-, and distortion-invariant texture features. In the experiments, DPCNN has been well tested by using Brodatz's album and the VisTex database. Several existing models are compared with the proposed DPCNN model. The experimental results, based on different testing data sets for images with different translations, orientations, scales, and affine transformations, show that our proposed model outperforms existing models in geometry-invariant texture retrieval. Furthermore, the robustness of DPCNN to noisy data is examined in the experiments.
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Lukashevich, M. M., and R. Kh Sadykhov. "TEXTURE CLUSTERING OF SATELLITE IMAGES USING SELF-ORGANIZING NEURAL NETWORK." International Journal of Computing, August 1, 2014, 15–21. http://dx.doi.org/10.47839/ijc.7.3.519.
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The goal of this paper is to present a texture clustering system for remote sensing image data. Texture information is useful for image data browsing and retrieval. Authors present the results of self-organizing neural network design for solving the clustering task of gray scale remote sensing image data. The architecture of neural network and the learning algorithms for this network such as: algorithm WTA (Winner Takes All), algorithm CWTA (Winner Takes All with Conscience) and classic Kohonen algorithm WTM (Winner Takes Most - the Winner receives more) are considered. Some experimental results using textures of the Brodatz album, multi-spectral and radar images are also represented.
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Sreeja Mole, S. S., and L. Ganesan. "UNSUPERVISED TEXTURE CLASSIFICATION OF ENTROPY BASED LOCAL DESCRIPTOR USING K-MEANS CLUSTERING ALGORITHM." International Journal of Computing, December 20, 2011, 133–40. http://dx.doi.org/10.47839/ijc.10.2.743.
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This paper presents an efficient approach for unsupervised Texture Segmentation and Classification, based on features extracted from entropy based local descriptor using K-means clustering with spatial information. The K- means clustering algorithm is commonly used in computer vision as a form of image segmentation. Texture analysis refers to a class of mathematical procedures and models that characterizes the spatial variations within imagery as a means of extracting information. Texture analysis may require the solution of two different problems first is Segmentation and Classification of a given image according to the different texture and second was for of a given texture with respect to a set of known textures. Based on the proposed concept, this paper describes the entropy based local descriptor using K-Means with spatial information approach. Experimental results show that the proposed framework performs very well compared to other clustering algorithms in all measured criteria. Spatial information has been effectively used for unsupervised texture classification for Brodatz of texture images. The model is not specifically confined to a particular texture feature. We tested this algorithm using other texture features. The proposed entropy based local descriptor approach gives good accuracy when compared with other methods.