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A new image classification method using interval texture feature and improved Bayesian classifier
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AI
DOI：10.1007/s11042-022-13531-6.png)
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Abstract
En 中文
In this paper, a novel technique for image classification is proposed with the three main contributions. First, we give the texture extraction technique for each image to have the two-dimensional interval based on the Grey Level Co-occurrence matrices. Second, the automatic fuzzy clustering algorithm for interval data to determine the prior probability for the classification problem by Bayesian method is created. Finally, the new principle to classify for image is established. Combining the above three improvements, we have the effective method to classify the images. In addition, the proposed method can be performed rapidly for the real data by the established Matlab procedure. Four image data sets with the different characters are used to illustrate the proposed method, and to compare to the well-known algorithms like Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Fisher method, Naive Bayes, Multi-Supported Vector Machine (Multi-SVM), Convolutional Neural Networks (CNN), and VGG-19. The results show that the proposed method has the good and stable empirical error, and give the outstanding result about time cost.
Keywords:
Classification
Extraction
Image
Interval data
Overlap distance
Journal
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3
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2.0W
Citations:
3.2W
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