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
More filters
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)
1 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]....
[...]
...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]....
[...]
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
More filters
01 Nov 1973
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Abstract: Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
20,442 citations
"A Multi-Class Fisher Linear Discrim..." refers methods in this paper
...These include feature extraction using co-occurrence matrices [15, 30], feature extraction using Gabor filters [5, 6] and Markov random field modeling [1, 21, 23, 28, 36]....
[...]
Book•
01 Jan 1972
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Abstract: This completely revised second edition presents an introduction to statistical pattern recognition Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field Each chapter contains computer projects as well as exercises
10,526 citations
"A Multi-Class Fisher Linear Discrim..." refers methods in this paper
...5112 © 2019 The Author (s); Helix E-ISSN: 2319-5592; P-ISSN: 2277-3495 The iterative multi-class Fisher linear discriminant (MFLD) is used to increase the separation between two different classes and to reduce a spread within a class [8, 11]....
[...]
Book•
01 Jan 1993
TL;DR: The digitized image and its properties are studied, including shape representation and description, and linear discrete image transforms, and texture analysis.
Abstract: List of Algorithms. Preface. Possible Course Outlines. 1. Introduction. 2. The Image, Its Representations and Properties. 3. The Image, Its Mathematical and Physical Background. 4. Data Structures for Image Analysis. 5. Image Pre-Processing. 6. Segmentation I. 7. Segmentation II. 8. Shape Representation and Description. 9. Object Recognition. 10. Image Understanding. 11. 3d Geometry, Correspondence, 3d from Intensities. 12. Reconstruction from 3d. 13. Mathematical Morphology. 14. Image Data Compression. 15. Texture. 16. Motion Analysis. Index.
5,451 citations
"A Multi-Class Fisher Linear Discrim..." refers background or methods in this paper
...Texture has over a million applications [13, 30] in different domains as stated previously....
[...]
...These include feature extraction using co-occurrence matrices [15, 30], feature extraction using Gabor filters [5, 6] and Markov random field modeling [1, 21, 23, 28, 36]....
[...]
TL;DR: A texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system is presented, which is based on reconstruction of the input image from the filtered images.
2,351 citations
"A Multi-Class Fisher Linear Discrim..." refers background or methods in this paper
..., (Nc/4) √2 cycles/image width, where Nc is the number of columns in the image [6, 20]....
[...]
...center frequencies and orientations with reduced computational complexity [9, 10, 20, 24, 25, 29, 31]....
[...]
...Jain AK and Farrokhnia Farshid [20] proposed unsupervised texture segmentation using Gabor filters....
[...]
...Non-linear transformation of filter outputs [20], moments of Gabor filter outputs [3]....
[...]
...The widely used feature extraction approach for textures is multichannel filtering performed using Gabor filters [4, 5, 6, 9, 10, 20]....
[...]
TL;DR: An interpretation of image texture as a region code, or carrier of region information, is emphasized and examples are given of both types of texture processing using a variety of real and synthetic textures.
Abstract: A computational approach for analyzing visible textures is described. Textures are modeled as irradiance patterns containing a limited range of spatial frequencies, where mutually distinct textures differ significantly in their dominant characterizing frequencies. By encoding images into multiple narrow spatial frequency and orientation channels, the slowly varying channel envelopes (amplitude and phase) are used to segregate textural regions of different spatial frequency, orientation, or phase characteristics. Thus, an interpretation of image texture as a region code, or carrier of region information, is emphasized. The channel filters used, known as the two-dimensional Gabor functions, are useful for these purposes in several senses: they have tunable orientation and radial frequency bandwidths and tunable center frequencies, and they optimally achieve joint resolution in space and in spatial frequency. By comparing the channel amplitude responses, one can detect boundaries between textures. Locating large variations in the channel phase responses allows discontinuities in the texture phase to be detected. Examples are given of both types of texture processing using a variety of real and synthetic textures. >
1,582 citations
"A Multi-Class Fisher Linear Discrim..." refers background or methods in this paper
...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]....
[...]
...The widely used feature extraction approach for textures is multichannel filtering performed using Gabor filters [4, 5, 6, 9, 10, 20]....
[...]
...5111 © 2019 The Author (s); Helix E-ISSN: 2319-5592; P-ISSN: 2277-3495 The two-dimensional impulse response of Gabor filter is given in [4, 6]...
[...]