The Experts below are selected from a list of 36657 Experts worldwide ranked by ideXlab platform
Paul Fieguth - One of the best experts on this subject based on the ideXlab platform.
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A New Gabor Filter Based Kernel for Texture Classification with SVM
2016Co-Authors: Mahdi Sabri, Paul FieguthAbstract:Abstract. The performance of Support Vector Machines (SVMs) is highly dependent on the choice of a kernel function suited to the problem at hand. In particular, the kernel implicitly performs a feature selection which is the most important stage in any Texture Classification algorithm. In this work a new Gabor filter based kernel for Texture Classification with SVMs is proposed. The proposed kernel function is based on a Gabor filter decomposition and exploiting linear predictive coding (LPC) in each subband, and exploiting a filter selection method to choose the best filters. The proposed Texture Classification method is evaluated using several Texture samples, and compared with recently published methods. The comprehensive evaluation of the proposed method shows significant improvement in Classification error rate
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Texture Classification from random features
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012Co-Authors: Li Liu-he, Paul FieguthAbstract:Inspired by theories of sparse representation and compressed sensing, this paper presents a simple, novel, yet very powerful approach for Texture Classification based on random projection, suitable for large Texture database applications. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag-of-words model to perform Texture Classification; thus, learning and Classification are carried out in a compressed domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of Texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We have conducted extensive experiments on each of the CUReT, the Brodatz, and the MSRC databases, comparing the proposed approach to four state-of-the-art Texture Classification methods: Patch, Patch-MRF, MR8, and LBP. We show that our approach leads to significant improvements in Classification accuracy and reductions in feature dimensionality.
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sorted random projections for robust Texture Classification
International Conference on Computer Vision, 2011Co-Authors: Li Liu, Paul Fieguth, Gangyao Kuang, Hongbin ZhaAbstract:This paper presents a simple and highly effective system for robust Texture Classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, and (3) Support Vector Machines (SVMs) based Classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random projection (RP) technique, then comparing two different Texture image representations (histograms and signatures) with various kernels in the SVMs. We have tested our Texture Classification system on six popular and challenging Texture databases for exemplar based Texture Classification, comparing with 12 recent state-of-the-art methods. Experimental results show that our Texture Classification system yields the best Classification rates of which we are aware of 99.37% for CUReT, 97.16% for Brodatz, 99.30% for UMD and 99.29% for KTH-TIPS. Moreover, combining random features significantly outperforms the state-of-the-art descriptors in material categorization.
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ICCV - Sorted Random Projections for robust Texture Classification
2011 International Conference on Computer Vision, 2011Co-Authors: Li Liu, Paul Fieguth, Gangyao Kuang, Hongbin ZhaAbstract:This paper presents a simple and highly effective system for robust Texture Classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, and (3) Support Vector Machines (SVMs) based Classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random projection (RP) technique, then comparing two different Texture image representations (histograms and signatures) with various kernels in the SVMs. We have tested our Texture Classification system on six popular and challenging Texture databases for exemplar based Texture Classification, comparing with 12 recent state-of-the-art methods. Experimental results show that our Texture Classification system yields the best Classification rates of which we are aware of 99.37% for CUReT, 97.16% for Brodatz, 99.30% for UMD and 99.29% for KTH-TIPS. Moreover, combining random features significantly outperforms the state-of-the-art descriptors in material categorization.
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ACCV (1) - Compressed sensing for robust Texture Classification
Computer Vision – ACCV 2010, 2011Co-Authors: Li Liu, Paul Fieguth, Gangyao KuangAbstract:This paper presents a simple, novel, yet very powerful approach for Texture Classification based on compressed sensing. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag-of-words model to perform Texture Classification, thus learning and Classification are carried out in the compressed domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of Texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to the state-of-the-art in Texture Classification on four databases: CUReT, Brodatz, UIUC and KTH-TIPS. Our approach leads to significant improvements in Classification accuracy and reductions in feature dimensionality, exceeding the best reported results on CUReT, Brodatz and KTH-TIPS.
Hongbin Zha - One of the best experts on this subject based on the ideXlab platform.
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sorted random projections for robust Texture Classification
International Conference on Computer Vision, 2011Co-Authors: Li Liu, Paul Fieguth, Gangyao Kuang, Hongbin ZhaAbstract:This paper presents a simple and highly effective system for robust Texture Classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, and (3) Support Vector Machines (SVMs) based Classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random projection (RP) technique, then comparing two different Texture image representations (histograms and signatures) with various kernels in the SVMs. We have tested our Texture Classification system on six popular and challenging Texture databases for exemplar based Texture Classification, comparing with 12 recent state-of-the-art methods. Experimental results show that our Texture Classification system yields the best Classification rates of which we are aware of 99.37% for CUReT, 97.16% for Brodatz, 99.30% for UMD and 99.29% for KTH-TIPS. Moreover, combining random features significantly outperforms the state-of-the-art descriptors in material categorization.
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ICCV - Sorted Random Projections for robust Texture Classification
2011 International Conference on Computer Vision, 2011Co-Authors: Li Liu, Paul Fieguth, Gangyao Kuang, Hongbin ZhaAbstract:This paper presents a simple and highly effective system for robust Texture Classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, and (3) Support Vector Machines (SVMs) based Classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random projection (RP) technique, then comparing two different Texture image representations (histograms and signatures) with various kernels in the SVMs. We have tested our Texture Classification system on six popular and challenging Texture databases for exemplar based Texture Classification, comparing with 12 recent state-of-the-art methods. Experimental results show that our Texture Classification system yields the best Classification rates of which we are aware of 99.37% for CUReT, 97.16% for Brodatz, 99.30% for UMD and 99.29% for KTH-TIPS. Moreover, combining random features significantly outperforms the state-of-the-art descriptors in material categorization.
Li Liu - One of the best experts on this subject based on the ideXlab platform.
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sorted random projections for robust Texture Classification
International Conference on Computer Vision, 2011Co-Authors: Li Liu, Paul Fieguth, Gangyao Kuang, Hongbin ZhaAbstract:This paper presents a simple and highly effective system for robust Texture Classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, and (3) Support Vector Machines (SVMs) based Classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random projection (RP) technique, then comparing two different Texture image representations (histograms and signatures) with various kernels in the SVMs. We have tested our Texture Classification system on six popular and challenging Texture databases for exemplar based Texture Classification, comparing with 12 recent state-of-the-art methods. Experimental results show that our Texture Classification system yields the best Classification rates of which we are aware of 99.37% for CUReT, 97.16% for Brodatz, 99.30% for UMD and 99.29% for KTH-TIPS. Moreover, combining random features significantly outperforms the state-of-the-art descriptors in material categorization.
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ICCV - Sorted Random Projections for robust Texture Classification
2011 International Conference on Computer Vision, 2011Co-Authors: Li Liu, Paul Fieguth, Gangyao Kuang, Hongbin ZhaAbstract:This paper presents a simple and highly effective system for robust Texture Classification, based on (1) random local features, (2) a simple global Bag-of-Words (BoW) representation, and (3) Support Vector Machines (SVMs) based Classification. The key contribution in this work is to apply a sorting strategy to a universal yet information-preserving random projection (RP) technique, then comparing two different Texture image representations (histograms and signatures) with various kernels in the SVMs. We have tested our Texture Classification system on six popular and challenging Texture databases for exemplar based Texture Classification, comparing with 12 recent state-of-the-art methods. Experimental results show that our Texture Classification system yields the best Classification rates of which we are aware of 99.37% for CUReT, 97.16% for Brodatz, 99.30% for UMD and 99.29% for KTH-TIPS. Moreover, combining random features significantly outperforms the state-of-the-art descriptors in material categorization.
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ACCV (1) - Compressed sensing for robust Texture Classification
Computer Vision – ACCV 2010, 2011Co-Authors: Li Liu, Paul Fieguth, Gangyao KuangAbstract:This paper presents a simple, novel, yet very powerful approach for Texture Classification based on compressed sensing. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag-of-words model to perform Texture Classification, thus learning and Classification are carried out in the compressed domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of Texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to the state-of-the-art in Texture Classification on four databases: CUReT, Brodatz, UIUC and KTH-TIPS. Our approach leads to significant improvements in Classification accuracy and reductions in feature dimensionality, exceeding the best reported results on CUReT, Brodatz and KTH-TIPS.
S. Arivazhagan - One of the best experts on this subject based on the ideXlab platform.
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Rotation and Scale Invariant Texture Classification Using Gabor and Curvelet Transforms
International Journal of Tomography and Simulation, 2015Co-Authors: S. Arivazhagan, S. NirmalaAbstract:One of the challenging problems in image processing and computer vision is Texture Classification. The goal of Texture Classification is to assign a test sample to one of the set of known classes. s. This paper presents an efficient method for rotation and scale invariant Texture Classification using multi-resolution transform such as Gabor and Curvelet transform. Gabor transform is a very useful tool for recognizing Texture images, because of their optimal localization properties. The features (mean, standard deviation, entropy) are extracted from the Gabor transformed images and used for Texture Classification. The Curvelet transform is very effective for representing the edges and other singularities along the curves. The features such as mean, standard deviation are derived from curvelet sub bands. This proposed method with combined Gabor and Curvelet transform produces better Classification rate.
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Texture Classification using color local Texture features
2013 International Conference on Signal Processing Image Processing & Pattern Recognition, 2013Co-Authors: S. Arivazhagan, R. BenittaAbstract:This Paper proposes a new approach to extract the features of a color Texture image for the purpose of Texture Classification. Four feature sets are involved. Dominant Neighbourhood Structure (DNS) is the new feature set that has been used for color Texture image Classification. In this feature a global map is generated which represents measured intensity similarity between a given image pixel and its surrounding neighbours within a certain window. Addition to the above generated feature set, features obtained from DWT are added together with DNS to obtain an efficient Texture Classification. Also the proposed feature sets are compared with that of Gabor wavelet, LBP and DWT. The Texture Classification process is carried out with the robust SVM classifier. The experimental results on the CUReT database shows that the proposed method is an efficient method whose Classification rate is higher when compared with the other methods.
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Texture Classification USING CURVELET TRANSFORM
International Journal of Wavelets Multiresolution and Information Processing, 2007Co-Authors: S. Arivazhagan, T. G. Subash Kumar, L. GanesanAbstract:Texture Classification has long been an important research topic in image processing. Nowadays Classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, ridgelet transform which deal effectively with line singularities in 2D is introduced. But images often contain curves rather than straight lines, so curvelet transform is designed to handle it. It allows representing edges and other singularities along lines in a more efficient way when compared with other transforms. In this paper, the issue of Texture Classification based on curvelet transform has been analyzed. Features are derived from the sub-bands of the curvelet decomposition and are used for Classification for the four different datasets containing 20, 30, 112 and 129 Texture images respectively. Experimental results show that this approach allows high degree of success rate in Classification to be obtained.
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Texture Classification using ridgelet transform
Pattern Recognition Letters, 2006Co-Authors: S. Arivazhagan, L. Ganesan, T. G. Subash KumarAbstract:Texture Classification has long been an important research topic in image processing. Now a day's Classification based on wavelet transform is being very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, ridgelet transform which deal effectively with line singularities in 2-D is introduced. It allows representing edges and other singularities along lines in a more efficient way compared to wavelet transform. In this paper, the issue of Texture Classification based on ridgelet transform has been analyzed. Features are derived from the sub-bands of the ridgelet decomposition and are used for Classification for the four different datasets containing 20, 30, 112 and 129 Texture images respectively. Experimental results show that this approach allows obtaining high degree of success rate in Classification.
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Texture Classification using ridgelet transform
Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 1Co-Authors: S. Arivazhagan, L. Ganesan, T. G. Subash KumarAbstract:Texture Classification has long been an important research topic in image processing. Classification based on the wavelet transform has become very popular. Wavelets are very effective in representing objects with isolated point singularities, but failed to represent line singularities. Recently, a ridgelet transform which deals effectively with line singularities in 2-D is introduced. It allows representing edges and other singularities along lines in a more efficient way. In this paper, the issue of Texture Classification based on a ridgelet transform has been analyzed. Features are derived from sub-bands of the ridgelet decomposition and are used for Classification for a data set containing 20 Texture images. Experimental results show that this approach allows to obtain a high degree of success in Classification.
Yongsheng Dong - One of the best experts on this subject based on the ideXlab platform.
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Jumping and Refined Local Pattern for Texture Classification
IEEE Access, 2018Co-Authors: Tianyu Wang, Yongsheng Dong, Chunlei Yang, Lin Wang, Liang Lingfei, Lintao ZhengAbstract:The local binary pattern (LBP) model is a simple and effective method of Texture Classification, but it is sensitive to rotational and noisy images. Although many variants of LBP are proposed by scholars, there are still several urgent problems, such as poor noise and rotation immunity. In this paper, we propose a robust Texture descriptor, jumping and refined local pattern (JRLP) for Texture Classification. In particular, we first extract jumping local difference count pattern (JLDCP) consisting of second-order difference count pattern and diagonal difference count pattern to represent the jumping information in a local domain. To capture the detail information left by JLDCP, we extract a refined completed LBP (RCLBP). By concatenating the JLDCP and RCLBP, we build a JRLP-based robust Texture descriptor for Classification. Experimental results on four representative Texture databases (Brodatz, CUReT, UIUC, and VisTex) reveal that our proposed Texture Classification method is effective and robust for noise, rotation, scale, and illumination variants and outperforms six representative methods.
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Multi-scale counting and difference representation for Texture Classification
The Visual Computer, 2017Co-Authors: Yongsheng Dong, Feng Jinwang, Chunlei Yang, Wang Xiaohong, Lintao ZhengAbstract:Multi-scale analysis has been widely used for constructing Texture descriptors by modeling the coefficients in transformed domains. However, the resulting descriptors are not robust to the rotated Textures when performing Texture Classification. To alleviate this problem, we in this paper propose a multi-scale counting and difference representation (CDR) of image Textures for Texture Classification. Particularly, we first extract a single-scale CDR feature consisting of the local counting vector (LCV) and the differential excitation vector (DEV). The LCV is established to capture different types of textural structures using the discrete local counting projection, while the DEV is used to describe the difference information of Textures in accordance with the differential excitation projection. Finally, the multi-scale CDR feature of a Texture image is constructed by combining CDRs at different scales. Experimental results on Brodatz, VisTex, and Outex databases demonstrate that our proposed multi-scale CDR-based Texture Classification method outperforms five representative Texture Classification methods.
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Feature extraction through contourlet subband clustering for Texture Classification
Neurocomputing, 2013Co-Authors: Yongsheng DongAbstract:Abstract Feature extraction is an important processing procedure in Texture Classification. For feature extraction in the wavelet domain, the energies of subbands are usually extracted for Texture Classification. However, the energy of one subband is just a specific feature. In this paper, we propose an efficient feature extraction method for Texture Classification. In particular, feature vectors are obtained by c -means clustering on the contourlet domain as well as using two conventionally extracted features that represent the dispersion degree of contourlet subband coefficients. The c -means clustering algorithm is initialized via a nonrandom initialization scheme. By investigating these feature vectors, we employ a weighted L 1 - distance for comparing any two feature vectors that represent the corresponding subbands of two images and define a new distance between two images. According to the new distance, a k -Nearest Neighbor (kNN) classifier is utilized to perform Texture Classification, and experimental results show that our proposed approach outperforms five current state-of-the-art Texture Classification approaches.
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ICIC (2) - Texture Classification based on contourlet subband clustering
Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 2012Co-Authors: Yongsheng DongAbstract:In this paper, we propose a novel Texture Classification method based on feature extraction through c-means clustering on the contourlet domain. In particular, all the features representing each contourlet subband are extracted by a c-means clustering standard algorithm. By investigating these features, we use the weighted L1 -norm for comparing the features of the two corresponding subbands of two images and define a new distance between two images. According to the new distance, a k-Nearest Neighbor (kNN) classifier is utilized to perform Texture Classification (TC), and experimental results reveal that our proposed approach outperforms two current state-of-the-art Texture Classification approaches.
