Fuzzy clustering image segmenting method with transfer learning function

Abstract

The invention discloses a fuzzy clustering image segmenting method with transfer learning function. The method adopts the classic fuzzy C fuzzy-means algorithm as the study object and is special for overcoming the shortcoming of the C fuzzy-means algorithm that the low capacity is provided for resisting the noise while facing the image with noise. During processing the new image, the image segmenting method is mainly carried out for the image with noise pollution. With the adoption of the fuzzy clustering image segmenting method disclosed by the invention, the reliable clustering information obtained by summarizing lots of past similar images under the C fuzzy-means algorithm can be effectively learnt and utilized, such information is always considered as the clustering centre; by introducing the reliable information into the current new image segmenting task, the current clustering task can be effectively guided, and the noise resisting effect can be achieved, therefore, more precise clustering centre and more precise image segmenting result can be obtained.

Description

technical field [0001] The invention belongs to the field of image processing and application, in particular to a fuzzy clustering image segmentation method with transfer learning ability. Background technique [0002] Since its introduction in 1995, transfer learning theory has produced a huge impact in the field of machine learning. This method subverts traditional machine learning methods and makes machine learning more intelligent (Pan J.L., Yang Q., A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 10, 2010: 1345-1359). Its specific performance is that when building a model using this theory, it will consider the existing similar models, use the previous model as a reference body, and then combine the current environment for modeling. Such a new modeling method will greatly improve the early stage. Compared with the traditional modeling method that does not consider historically similar scenes and only considers the current scene and s...

AI Technical Summary

Problems solved by technology

However, due to the fact that this technology ignores the spatial information of the samples, the segmentation accuracy of this type of technology is often unsatisfactory in the face of noise-contaminated images.

For this kind of scene, there are many related improvement technical solutions. The general solution is to first denoise the image, and then use the fuzzy C-means algorithm to perform cluster analysis on the processed image. It is also widely used in the field of processing, but this method usually results in the loss of image information in the process of denoising due to the difference in the selected denoising algorithm, thereby destroying the information components of the entire image, resulting in low image segmentation accuracy. Phenomenon

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