Research On Image Segmentation Algorithm Based On Fuzzy Clustering

Image segmentation is one of the important techniques in computer vision and image analysis.Although many researchers at home and abroad has carried out a lot of work on image segmentation,developed a number of different image segmentation algorithms,but most of the segmentation algorithm is only for a particular object segmentation,today there is no general segmentation algorithm,so the researchers are continuing efforts to research and explore the segmentation algorithm.Researchers have successfully introduced the fuzzy set theory into the image segmentation process of the hard clustering method,resulting in the image segmentation algorithm of soft clustering.The most widely used and most perfect soft clustering algorithm is fuzzy c-means clustering algorithm.However,the fuzzy c-means clustering algorithm is very sensitive to the abnormal pixels in the image segmentation,and it is easy to fall into the local extrema in the early stage,so it needs to determine the number of clusters in advance.In view of the above considerations,a FCM image segmentation algorithm based on spatial information is proposed in this paper because the fuzzy c-means clustering algorithm is very sensitive to noise and outliers in the image.The algorithm can effectively utilize the spatial information in the image and the gray level information of the image to be segmented to improve the segmentation accuracy of the noise image.Firstly,the spatial function is introduced into the membership function and the membership weight of each pixel is changed to suppress the noise.Then,taking the neighborhood information of the pixels in the image into consideration,each pixel is processed to form a linear weighted sum image,and the gray histogram of the newly formed image is clustered.The experimental results of synthetic and real images show that the segmentation accuracy of the proposed algorithm for noise images is above 99%.Compared with other algorithms,the proposed algorithm can remove noise while maintaining important details of the image,and has robustness for noise images.In order to avoid the traditional fuzzy c-means algorithm falling into local extremum,this paper puts forward a fuzzy c-means image segmentation algorithm based on bat algorithm based on the global feature of meta-heuristic algorithm.Firstly,chaotic mapping is introduced to improve the bat algorithm and improve its global search ability.Then the improved bat algorithm is applied to obtain the clustering center and the number of clusters.Finally,FCM algorithm achieves image segmentation by clustering the pixels in the image according to the clustering center.In this paper,the algorithm of fuzzy clustering image segmentation is experimentally discussed,and its correctness is verified by the segmentation results of multiple images and objective numerical analysis.