A Multi-Class Fisher Linear Discriminant Approach for the Improvement in the Accuracy of Complex Texture Discrimination
Sanjaykumar Kinge,B. Sheela Rani,Mukul S. Sutaone +2 more
- Vol. 9, Iss: 4, pp 5108-5121
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TLDR
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.read more
Citations
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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.
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Restored texture segmentation using Markov random fields.
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.
References
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