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Image segmentation based on two dimensional maximum entropy and teaching-learning-based optimization
Author(s): 
Pages: 116-121,133
Year: Issue:  7
Journal: Video Engineering

Keyword:  maximum entropyteaching-learning-based optimization(TLBO)image segmentation;
Abstract: As a global thresholding method,the two-dimensional maximum entropy method is used to consider the image gray level and spatial information,and the image segmentation results can be obtained in the case of low SNR.In order to improve the segmentation speed and efficiency in computation,on the basic of two-dimensional maximum entropy theory,the paper proposed a nonlinear inertia weight teach-learn-based optimization method to optimize the two-dimensional maximum entropy,the method take the 2-D maximum entropy asadaptive degree function of teaching-learning-based optimization algorithm,use the optimal threshold after optimizing to segment image.Due tothe nonlinear inertia weight teaching-learning-based optimization method has less parameter,and the convergence rate is fast,and the optimal segmentation threshold can be determined quickly through continuous optimization.The experimental results show that this method is not only fast and accurate,but also has strong adaptability.
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