A Mixture Model of Circular-Linear Distributions for Color Image Segmentation
نویسندگان
چکیده
This article deals with mixture model based color image segmentation in the LCH color space. In this space, one of the components (representing hue in particular) is circular in nature. Hence LCH image pixels are samples on a cylinder. A statistical model for such data needs to employ circular-linear joint distributions. Here such a model is designed using the "Independent von-Mises Gaussian" distribution. Further its mixture is used to approximate the distribution of the LCH data. The mixture parameters are estimated using standard EM algorithm. Comprehensive experiments are conducted on Berkeley segmentation data set to measure the performance of the algorithm in terms of a variety of quantitative indices for image segmentation. A comparison is further made with some existing mixture models. Our study reveals that the proposed mixture model performs satisfactorily in this regard.
منابع مشابه
Image Segmentation using Gaussian Mixture Model
Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...
متن کاملStatistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation
Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wa...
متن کاملIMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL
Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm. In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...
متن کاملRobust Method for E-Maximization and Hierarchical Clustering of Image Classification
We developed a new semi-supervised EM-like algorithm that is given the set of objects present in eachtraining image, but does not know which regions correspond to which objects. We have tested thealgorithm on a dataset of 860 hand-labeled color images using only color and texture features, and theresults show that our EM variant is able to break the symmetry in the initial solution. We compared...
متن کاملPerformance Analysis of Segmentation of Hyperspectral Images Based on Color Image Segmentation
Image segmentation is a fundamental approach in the field of image processing and based on user’s application .This paper propose an original and simple segmentation strategy based on the EM approach that resolves many informatics problems about hyperspectral images which are observed by airborne sensors. In a first step, to simplify the input color textured image into a color image without tex...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012