Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the segmentation process. Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo-metric for comparing feature distributions. A region-based algorithm is developed for coarse image segmentation and a pixelwise classification scheme for improving localization of region boundaries. The performance of the method is evaluated with various types of test images. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
['Timo Ojala', 'Matti Pietikäinen']
I am writing a research paper on a idea Unsupervised Texture Segmentation Using Feature Distributions
[{"from": "human", "value": "I am writing a research paper on a idea Unsupervised Texture Segmentation Using Feature Distributions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the segmentation process. Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo-metric for comparing feature distributions. A region-based algorithm is developed for coarse image segmentation and a pixelwise classification scheme for improving localization of region boundaries. The performance of the method is evaluated with various types of test images. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved."}]