Abstract
Texture feature is one of the most common image segmentation, classification, extraction, and surface analysis techniques. In many image processing tasks, texture plays a vital role. The texture is defined by a neighborhood’s spatial distribution of the gray level. For a point, however, texture cannot be described. The resolution at which an image is presented defines the perceived size of the texture. Image texture provides us with details on the color or intensity spatial arrangement in a picture or area chosen for a picture. Textures of images can be created artificially or found in natural scenes captured in an image. Nowadays, Gabor filtering has been widely used for texture feature extraction. In our paper, we presented a well-ordered extraction method of two-dimensional texture features and a comparison study between Gabor filer and Log Gabor filter. First, we converted the image to the gray level. Then on each converted part of the gray-level image, a two-dimensional Log Gabor filter with different frequencies decomposed with the SVD algorithm is applied to extract suitable distinctive texture information. We used SVD’s unique values as a function vector to test the output of the proposed model. We used the Naïve Bayes classifier to train and test my experimental dataset for classifiers. We did the same things with Gabor filter and found a lower accuracy rate than Log Gabor filter.
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Chakraborty, P., Yousuf, M. A., & Rahman, S. (2021). Predicting level of visual focus of human’s attention using machine learning approaches. In Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Springer, Singapore 683–694.
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Chakraborty, P., Yousuf, M.A., Islam, S., Khatun, M., Sarker, A., Rahman, S. (2022). Analysis of Texture Feature Extraction Technique in Image Processing. In: Elhoseny, M., Yuan, X., Krit, Sd. (eds) Distributed Sensing and Intelligent Systems. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-64258-7_56
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