On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Model-Based image segmentation plays a dominant role in image analysis and image retrieval. To analyze the features of the image, model based segmentation algorithm will be more efficient compared to non-parametric methods. In this paper, we proposed Automatic Image Segmentation using Wavelets (AISWT) to make segmentation fast and simpler. The approximation band of image Discrete Wavelet Transform is considered for segmentation which contains significant information of the input image. The Histogram based algorithm is used to obtain the number of regions and the initial parameters like mean, variance and mixing factor. The final parameters are obtained by using the Expectation and Maximization algorithm. The segmentation of the approximation coefficients is determined by Maximum Likelihood function. It is observed that the proposed method is computationally efficient allowing the segmentation of large images and performs much superior to the earlier image segmentation methods.

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Automatic Image Segmentation using Wavelets

We proposed a novel method for face matching from face image database. In our method we have taken set of face images so recognition decisions need to be based on comparisons of face image database. This paper presents an approach to region based face matching. The low level image segmentation method mean shift is used to divide the image into many small regions. As a popular segmentation scheme for color image, watershed has over segmentation as compared to mean-shift and also mean-shift preserves well the edge information of the object. The proposed method automatically merges the regions that are initially segmented by mean shift segmentation, effectively extracts the object contour and then, matches the obtained mask with test database image sets on the basis of color and texture. Extensive experiments are performed and the results show that the proposed scheme can reliably form the mask from the face image and effectively matches the mask with face image sets.

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A Novel Face Matching Technique using Mean-Shift with Region Merging

Image segmentation is to classify or cluster an image into several parts (regions) according to the feature of image. This paper presents a survey of different image segmentation techniques. Efficient and effective image segmentation is an important task in computer vision and object recognition. Since fully automatic image segmentation is usually very hard for natural images, interactive schemes with a few simple user inputs are good solutions. In this paper, several segmentation techniques using different segmentation algorithms like Watershed, graph based, mean-shift etc with dynamic region merging approach are taken from the literature are reviewed. In this paper, the main aim is to understand that which algorithm works best with dynamic region merging for image segmentation.

http://www.ijarcsee.org/index.php/IJARCSEE/article/download/203/190

Image Segmentation by using Mean-Shift with Dynamic Region Merging - A Survey

Medical imaging acts as a significant function on medical informatics and it is listened on recognition and categorization of diseases. Variable nature of image features, such as size, shape, intensity, color, texture etc., cause complexity in the image segmentation and analysis of the image nature. To overcome the segmentation issues, our previous work presented unsupervised learning model to extract features from the medical images based on scale invariant feature transformation. This would enrich the features extracted from the medical image for segmentation. But the previous work does not discuss about the segmentation for selected spatial and hierarchical features and provides less efficiency in performance. To enhance the process, in this work, a schematic procedure is used for segmentation of images based on different sets of selected features from the unsupervised learning model of extracted features. Feature selection on the medical images is done on the basis of automatic subjectiveoptimality model. Subjective optimality refers to the context of image analysis to be made i.e., tumor, non-tumor, and edema dependent feature sets. The experimental performance is evaluated with benchmark data sets extracted from research repositories of both real and synthetic data sets. The performance parameter used for the analysis of the proposed feature selection on segmented image using automatic subjective optimality model are Feature transformation factor, Selection size, Image Subjective-Optimal ratio.

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Feature Selection on Segmented Image using Automatic Subjective Optimality Model

Summary In Image Processing Model Based Image Segmentation plays a dominant role in Image Analysis and Image Retrieval . Recently much work has been reported regarding Image Segmentation based on Finite Gaussian Mixture Models using EM algorithm. (Yiming Wu et al (2003)) , (Yamazaki.T (1998)). However, in some images the pixel intensities inside the image regions may not be Meso- Kurtic or Bell Shaped, because of the NonGaussian nature . Hence there are some situations where Image Segmentation is to be done with a more Generalized Finite Mixture Distribution. In this article we develop and analyze an image segmentation method based on Finite Generalized Gaussian Mixture Model using EM and K-Means algorithm. The K-Means algorithm is utilized to obtain the number of regions and the initial estimates of the model parameters. The update equations of the model parameters are obtained by using the EM algorithm. The segmentation of the pixels in the image is done by maximizing the component likelihood function. The performance of this method is evaluated through real time data on 3 images by calculating misclassification rate and image quality metrics. It is observed that the proposed method performs much superior to the earlier image segmentation methods.

/pdf/unsupervised-image-segmentation-method-based-on-finite-2mz6kfujmg.pdf

Srinivas Rao

Papers

Unsupervised Image Segmentation Method based on Finite Generalized Gaussian Distribution with EM & K-Means Algorithm