A Multi-Class Fisher Linear Discriminant Approach for the Improvement in the Accuracy of Complex Texture Discrimination

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Sanjaykumar Kinge

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.

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How to Cite
Kinge, S. . (2019). A Multi-Class Fisher Linear Discriminant Approach for the Improvement in the Accuracy of Complex Texture Discrimination. Helix, 9(04), 5108- 5121. Retrieved from https://helixscientific.pub/index.php/home/article/view/5
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