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.

No abstract

No abstract

<abstract> <p>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.</p> </abstract>