Optimization of Image Segmentation Method Based on Improved Wavelet Algorithm

Abstract

In the optimization image segmentation process, the traditional algorithm is used for segmentation, because the calculation method is complex, image segmentation accuracy rate is low, the clarity is not high, when the amount of information in the image segmentation is very large, the time consumption is big. Aiming at the above problems, an improved image optimization segmentation method is proposed based on wavelet algorithm. The image feature is obtained based on wavelet transform, the image characteristics can be reacted in local information, the advantage is obvious, the adjacent scale wavelet coefficients multiplication method isused to remove the noise, and the wavelet algorithm and graph theory algorithm is fused to generate the minimum spanning tree, the accurate image segmentation is obtained with minimum spanning tree. The simulations show the method is applied in image segmentation, the precision is high, and it has strong applicability. Introduction In the research field of image, the technology of image segmentation is a kind of very important technology of image processing, it is widely applied in many aspects of image processing, the general idea of image segmentation technology is to put a large image to divideinto many small pieces, previous research shows the methods and types of image segmentation is great many. But there are no unified standard to measure, this has brought trouble tothe large-scale application. So how to establish a reasonable optimization of general image segmentation method is the key to solve the issue, more and more experts have put a lot ofrelated attention to the field. At this stage, the main methods of image segmentation include ant colony algorithm, image segmentation method based on pixel gray difference algorithm, and image segmentation method based on adaptive threshold segmentation algorithm. Among them, the most commonly used method is the image segmentation based on adaptive thresholdsegmentation algorithm. According to the traditional image segmentation methods, the image surface is core, this method is used in segmentation, it has strong operability, it is simple and it has obtained the broad application. But its disadvantage is that the surface is often leads to excessive segmentation, the segmentation performance is bad. The computational complexity is big[1]. Therefore, an improved image optimization segmentation method is proposed based on wavelet algorithm. The image feature is obtained based on wavelet transform, the image characteristics can be reacted in local information, the advantage is obvious, the adjacent scale wavelet coefficients multiplication method is used to remove the noise, and the wavelet algorithm and graph theory algorithm is fused to generate the minimum spanning tree, the accurate image segmentation is obtained with minimum spanning tree. The simulations show the method is applied in image segmentation, the precision is high, and it has strong applicability. Image segmentation method based on improved wavelet algorithm A Image feature extraction and denoising.The advantage of wavelet transform is that it can provide the time domain and frequency domain window with the image features. It has the ability of reaction of the local features of image information, so it can consider to extract local features of images by using the characteristics of wavelet transform, wavelet transform is used for image International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2015) © 2015. The authors Published by Atlantis Press 1019 processing, according to the first object of study by wavelet analysis, the wavelet analysis itself has no deformation[2], the wavelet analysis and moment invariants are combined, and the wavelet moment invariant is used to mine the details of the image control ability, in order to obtain the rotation invariant moment, the formula is adopt as: For the continuous grey function ( ) , f x y , the continuous gray function is particularly effective to the moment rotation and the details of the image processing, its ( ) p q + order two dimension origin moment pq M is defined as: ( ) -