Color Image Segmentation Algorithm Based On Spectral Clustering

Image segmentation is the basic unresolved problem in the field of image recognition, which is' also one of difficulties in the image processing. With regard to the domain of computer vision, image segmentation is a procedure that dividing the image into several sub-regions to express the different objects, which is intended to simplify or change the representation form of image so as to make it easier for the analysis and understanding of image. It is a key step from the image processing to image analysis. Current methods of colored images segmentation are mainly composed of the approaches of gray image segmentation and the combination of alternation of color space, there are several common methods of gray image segmentation, such as the histogram threshold, the clustering method of feature space, the approach based on area, the method of edge detection, fuzzy method, neural network, the method of physical model and combinations of the above-mentioned methods. The common transformation of color space includes the RGB, YUV, LUV, HSI, LAB and combinations of the former spaces. Image segmentation based on clustering is a hot issue in the past decades. In particular, the spectral clustering algorithm not only has no restriction towards the distribution of sample space and converges to the global optimal solution, but also obtains a relatively satisfying segmentation effect in the color image segmentation. However, there are several unresolved problems in the spectral clustering algorithm itself such as definition of parameters, computation of high-throughput data, selection of cluster number and so on. Therefore the study on spectral clustering method is just a start.With regard to the segmentation methods of color images based on spectral clustering algorithm, this paper mainly analyzes and studies the color image segmentation from the two aspects of segmentation method of above-mentioned gray images and transformation of color space. The research work includes the following points:(l)Firstly, the essence of spectral clustering algorithm is to project the data from the higher dimensional space to lower dimensional space and obtain the novel data expression which makes the distribution of identical cluster much more compact. However, the traditional spectral clustering algorithm performs not well in dealing with the problems such as self defining parameters, processing the high-throughput data and automatically determining the cluster number. Therefore this paper proposes a self-adaptive spectral clustering algorithm based on the Nystrom method of multi-hierarchical structure, which combines the spectral clustering algorithm based on the method of multi-hierarchical structure with the approach based on the Nystrom sampling in order to effectively reduce the time consumed, solve the problem of out of memory during the computation procedure of high-throughput data, and automatically determine the cluster number by means of the k value of self-adaptive selection through analyzing the eigengap when executing the k-means clustering algorithm. The simulation experiment turns out that the self-adaptive spectral clustering algorithm based on the Nystrom method of multi-hierarchical structure is superior to spectral clustering method based on Nystrom approach in terms of time consumed and segmentation effect.(2)Secondly, the color space of RGB is non-linear and there are strong relationships among the three components. In addition, the existence of differential threshold of color makes it non-convenient for processing the color images in the RGB color space. Therefore the LUV color space is integrated to build up the coordinating color space with the human vision. Owing to that the LUV color space has the characters of consistency, homogeneity and irrelevances among the color components, it is widely applied to the field of color image processing of computer and obtains the better effect. The experiments prove that image segmentation in the LUV color space performs better from the perspective of both texture and marginal area of color images.(3)Finally, with the continuous increment of image resolution, the traditional segmentation algorithms of color images based on pixel consume too much time. So it is difficult for the color image segmentation to reduce the computation time of data. Although adopting the watershed algorithm to divide images generate the over segmentation, the marginal information can be better preserved, which transforms the drawback of over segmentation to the advantage in the watershed algorithm. Afterwards regards each small area which contains the marginal information as object to cluster by means of spectral clustering algorithm for the aim of reducing the computation time of data of spectral clustering method. Simultaneously, the coupled restriction information of must-link and cannot-link are introduced and adding the punishing item into k-means algorithm to confine the samples of disobeying the coupled restriction in order to further improve the segmentation accuracy of algorithm. The experiments turn out that the semi-supervising spectral clustering algorithm based on area can effectively reduce the consumed time of image segmentation and meantime improve the accuracy and stability of image segmentation.