A Multi-Class Fisher Linear Discriminant Approach for the Improvement in the Accuracy of Complex Texture Discrimination
01 Jul 2019-Vol. 9, Iss: 4, pp 5108-5121
TL;DR: An elaborated Fisher Linear Discriminant (FLD) based semi-supervised approach for improving the accuracy of segmentation of multi-class complex fine textures achieves the second rank for 21 benchmark images among the ten state-of-the-art algorithms.
Abstract: Texture segmentation has a wide spectrum of applications in diverse fields. This paper presents an elaborated Fisher Linear Discriminant (FLD) based semi-supervised approach for improving the accuracy of segmentation of multi-class complex fine textures. Gabor filter and local statistics (local variance) are used for feature extraction of texture images. Texture segments in the image are separated using K-means clustering. The results obtained using K-means clustering are refined by multi-class Fisher Linear Discriminant (MFLD). The algorithm is tested on wide varieties of several hundred homogenous and complex textures from five texture databases viz. Outex texture database, vision texture database (Vistex), Brodatz textures, Prague textures and Pertex texture database. Fisher distance (FD) is a measure of texture separability. Segmentation of complex textures is relatively a difficult task. The improvement in the segmentation accuracy of complex textures is achieved simply by the termination of MFLD based algorithm when Fisher distance (FD) ceases to increase with the increasing iterations of MFLD. After a quantitative analysis of the experimentation, it is concluded that the segmentation accuracy of complex textures and the combination of complex and homogeneous fine textures (with small texture primitives) increases as high as 29.83% with the increasing iterations of MFLD resulting in a significant improvement at the boundaries. Detailed results are provided in the experimentation and results section of the paper. The results achieve the second rank for 21 benchmark images among the ten state-of-the-art algorithms.
Citations
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01 May 2020
TL;DR: The present study offers the restoration of Prague texture database benchmark images corrupted with Gaussian noise with different variance values, using the state-of-the-art algorithm viz.
Abstract: Texture segmentation is a well-known research domain and a large number of researchers are working on it across the globe due to its wide varieties of applications in various domains such as medical imaging, shape extraction, product inspection, remote sensing, and segmentation of natural images Many times, images are corrupted by various types of noises such as Gaussian, speckle, and salt-pepper noise Most often Gaussian noise is a source of corruption in many applications Prague texture dataset is extensively used by researchers due to wide varieties of multi-class textures in it The present study offers the restoration of Prague texture database benchmark images These images are corrupted with Gaussian noise with different variance values The state-of-the-art algorithm viz Colour Block Matching 3D (C-BM3D) filter, which achieves the first rank among 15 algorithms reported in the most recent literature, is used for the restoration of noisy texture benchmark images Image restoration performance metrics used are peak signal to noise ratio (PSNR) and structural similarity index (SSIM)
1Â citations
Cites background or methods from "A Multi-Class Fisher Linear Discrim..."
...Gabor filter-based texture segmentation using classic classifiers is reported in [3, 11, 13, 20, 22, 23, 24]....
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...According to the literature reviewed here for the past three decades, it is found that texture benchmark images are not yet degraded with noise before segmentation [3, 8, 9, 13, 14, 17, 19, 20, 21, 22, 23, 24]....
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TL;DR: In this article , a three-phase approach is developed for the segmentation of textures contaminated by noise, in the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature, and the remaining two phases, segmentation is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the median filter based on segmentation performance metrics.
Abstract: Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc.
References
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TL;DR: It is noted here that simpler, lower-level mechanisms tuned for size may be sufficient to explain this discrimination of micropatterns based on the density of such features as terminators, corners, and intersections within the patterns.
Abstract: Texture perception has frequently been studied using textures constructed by repeated placement of micropatterns or texture elements. Theories have been developed to explain the discrimina-bility of such textures in terms of specific features within the micropatterns themselves. For example, Beck1,2 observed that a region filled with vertical Ts is readily distinguished from one filled with tilted Ts but not from one filled with vertical Ls. He attributed this to the different distribution of oriented line segments present in the former case but not in the latter. However, Bergen and Julesz3 found that a region of randomly oriented Xs segregated from one filled with randomly oriented Ls, in spite of the identical distribution of oriented line segments in the two cases. They suggested that this discrimination might be based on the density of such features as terminators, corners, and intersections within the patterns. We note here that simpler, lower-level mechanisms tuned for size may be sufficient to explain this discrimination. We tested this by varying the relative sizes of the Xs and the Ls; when they produce equal responses in size-tuned mechanisms they are hard to discriminate, and when they produce different size-tuned responses they are easy to discriminate.
353Â citations
01 Jan 1996
TL;DR: Experiments show that the HEC network leads to a significant improvement in the clustering results over the K-means algorithm with Euclidean distance, and indicates that hyperellipsoidal shaped clusters are often encountered in practice.
Abstract: We propose a self-organizing network for hyperellipsoidal clustering (HEC). It consists of two layers. The first employs a number of principal component analysis subnetworks to estimate the hyperellipsoidal shapes of currently formed clusters. The second performs competitive learning using the cluster shape information from the first. The network performs partitional clustering using the proposed regularized Mahalanobis distance, which was designed to deal with the problems in estimating the Mahalanobis distance when the number of patterns in a cluster is less than or not considerably larger than the dimensionality of the feature space during clustering. This distance also achieves a tradeoff between hyperspherical and hyperellipsoidal cluster shapes so as to prevent the HEC network from producing unusually large or small clusters. The significance level of the Kolmogorov-Smirnov test on the distribution of the Mahalanobis distances of patterns in a cluster to the cluster center under the Gaussian cluster assumption is used as a compactness measure. The HEC network has been tested on a number of artificial data sets and real data sets, We also apply the HEC network to texture segmentation problems. Experiments show that the HEC network leads to a significant improvement in the clustering results over the K-means algorithm with Euclidean distance. Our results on real data sets also indicate that hyperellipsoidal shaped clusters are often encountered in practice.
287Â citations
"A Multi-Class Fisher Linear Discrim..." refers background or methods in this paper
...The literature survey clearly indicates that many researchers used Gabor filter for feature extraction with non-optimal parameters [9, 10, 20, 24, 25, 29, 31]....
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...Mao and Jain AK [24] proposed neural network-based supervised texture segmentation using Gabor filters....
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...The approach elaborated in this paper uses Gabor filters [3, 4, 5, 6, 9, 10, 20, 24, 25, 28, 29, 31, 36] for feature extraction, which is a slightly improved version of the algorithm proposed in [5]....
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...center frequencies and orientations with reduced computational complexity [9, 10, 20, 24, 25, 29, 31]....
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TL;DR: The proposed IRGS method provides the possibility of building a hierarchical representation of the image content, and allows various region features and even domain knowledge to be incorporated in the segmentation process.
Abstract: This paper proposes an image segmentation method named iterative region growing using semantics (IRGS), which is characterized by two aspects. First, it uses graduated increased edge penalty (GIEP) functions within the traditional Markov random field (MRF) context model in formulating the objective functions. Second, IRGS uses a region growing technique in searching for the solutions to these objective functions. The proposed IRGS is an improvement over traditional MRF based approaches in that the edge strength information is utilized and a more stable estimation of model parameters is achieved. Moreover, the IRGS method provides the possibility of building a hierarchical representation of the image content, and allows various region features and even domain knowledge to be incorporated in the segmentation process. The algorithm has been successfully tested on several artificial images and synthetic aperture radar (SAR) images.
223Â citations
"A Multi-Class Fisher Linear Discrim..." refers background or methods in this paper
...Although they achieved good results, it was at a heavy computational cost and a complex theoretical MRF segmentation model....
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...This section describes contributions of different researchers to texture segmentation and image segmentation approaches performed using Gabor filters and Markov random field (MRF) over the past twenty-eight years in a chronological order....
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...Yu Q and Clausi DA [34, 35] proposed an MRF based segmentation algorithm for highly non-stationary images....
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...The second category in the literature survey includes texture segmentation using optimal Gabor filter parameters and Fisher discriminant [5,6] and segmentation of texture and sea ice synthetic aperture radar (SAR) images using a model-based approach based on MRF [7, 28, 34, 35]....
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...Some of the researchers used a computationally expensive and theoretically complex approach using MRF for the segmentation of textures and other images [7, 28, 34, 35]....
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TL;DR: A novel method for efficient image analysis that uses tuned matched Gabor filters that requires no a priori knowledge of the analyzed image so that the analysis is unsupervised.
Abstract: Recent studies have confirmed that the multichannel Gabor decomposition represents an excellent tool for image segmentation and boundary detection. Unfortunately, this approach when used for unsupervised image analysis tasks imposes excessive storage requirements due to the nonorthogonality of the basis functions and is computationally highly demanding. In this correspondence, we propose a novel method for efficient image analysis that uses tuned matched Gabor filters. The algorithmic determination of the parameters of the Gabor filters is based on the analysis of spectral feature contrasts obtained from iterative computation of pyramidal Gabor transforms with progressive dyadic decrease of elementary cell sizes. The method requires no a priori knowledge of the analyzed image so that the analysis is unsupervised. Computer simulations applied to different classes of textures illustrate the matching property of the tuned Gabor filters derived using our determination algorithm. Also, their capability to extract significant image information and thus enable an easy and efficient low-level image analysis will be demonstrated. >
206Â citations
"A Multi-Class Fisher Linear Discrim..." refers background or methods in this paper
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...The approach elaborated in this paper uses Gabor filters [3, 4, 5, 6, 9, 10, 20, 24, 25, 28, 29, 31, 36] for feature extraction, which is a slightly improved version of the algorithm proposed in [5]....
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...center frequencies and orientations with reduced computational complexity [9, 10, 20, 24, 25, 29, 31]....
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...Teuner Andreas et al [31] proposed unsupervised texture segmentation using Gabor filters....
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01 Jan 1994
TL;DR: LTS1 Reference LTS-ARTICLE-1994-002 Record created on 2006-06-14, modified on 2016-08-08.
Abstract: Keywords: LTS1 Reference LTS-ARTICLE-1994-002 Record created on 2006-06-14, modified on 2016-08-08
156Â citations
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