Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and KMeans
31 Jul 2011-International Journal of Computer Applications (Foundation of Computer Science (FCS))-Vol. 25, Iss: 4, pp 5-13
TL;DR: A new Image Segmentation method based on Finite Doubly Truncated Bivariate Gaussian Mixture Model that outperforms the existing model based image segmentation methods.
Abstract: A new Image Segmentation method based on Finite Doubly Truncated Bivariate Gaussian Mixture Model is proposed in this paper. The Truncated Bivariate Gaussian Distribution includes several of the skewed and asymmetric distributions as particular cases with finite range. This distribution also includes the Gaussian distribution as a limiting case. We use Expectation maximization (EM) algorithm to estimate the model parameters of the image data and the number of mixture components is estimated by using K-means Clustering algorithm. The K-means clustering algorithm is also utilized for developing the initial estimates of the EM algorithm. The segmentation is carried out by clustering of feature vector into appropriate component according to the Maximum Likelihood Estimation criteria. The advantage of our method lies its efficiency on initialization of the model parameters and segmenting the images in a totally unsupervised manner. The performance of the proposed algorithm is studied by computing the segmentation performance measures like, PRI, GCE and VOI. The ability of this method for image retrieval is demonstrated by computing the image quality metrics for six images namely OSTRICH, POT, TOWER, BEARS, DEER and BIRD. The experimental results show that this method outperforms the existing model based image segmentation methods.
Citations
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TL;DR: The application of multivariate generalized Gaussian mixture probability model for segmenting the texture of an image integrating with hierarchical clustering, developed using component maximum likelihood under Bayesian frame is addressed.
Abstract: Texture deals with the visual properties of an image. Texture analysis plays a dominant role for image segmentation. In texture segmentation, model based methods are superior to model free methods with respect to segmentation methods. This paper addresses the application of multivariate generalized Gaussian mixture probability model for segmenting the texture of an image integrating with hierarchical clustering. Here the feature vector associated with the texture is derived through DCT coefficients of the image blocks. The model parameters are estimated using EM algorithm. The initialization of model parameters is done through hierarchical clustering algorithm and moment method of estimation. The texture segmentation algorithm is developed using component maximum likelihood under Bayesian frame. The performance of the proposed algorithm is carried through experimentation on five image textures selected randomly from the Brodatz texture database. The texture segmentation performance measures such as GCE, PRI and VOI have revealed that this method outperform over the existing methods of texture segmentation using Gaussian mixture model. This is also supported by computing confusion matrix, accuracy, specificity, sensitivity and F-measure.
5 citations
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...This also includes Gaussian distribution as a particular case [12]....
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01 Jan 2012
TL;DR: In this paper, a novel and new skin color segmentation algorithm is proposed based on bivariate Pearson type II a for human computer interaction, which is one of the most important segmentation algorithms.
Abstract: Probability distributions formulate the basic framework for developing several segmentation algorithms. Among the various segmentation algorithms, skin colour segmentation is one of the most important algorithms for human computer interaction. Due to various random factors influencing the colour space, there does not exist a unique algorithm which serve the purpose of all images. In this paper a novel and new skin colour segmentation algorithms is proposed based on bivariate Pearson type II a
5 citations
01 Jan 2014
TL;DR: The survey of the skin pixel segmentation using the learning algorithms is presented and it is shown that skin classifier identifies the boundary of theskin image in a skin color model based on the training dataset.
Abstract: Skin segmentation is the process of the identifying the skin pixels in a image in a particular color model and dividing the images into skin and non-skin pixels. It is the process of find the particular skin of the image or video in a color model. Finding the regions of the images in human images to say these pixel regions are part of the image or videos is typically a preprocessing step in skin detection in computer vision, face detection or multiview face detection. Skin pixel detection model converts the images into appropriate format in a color space and then classification process is being used for labeling of the skin and non-skin pixels. A skin classifier identifies the boundary of the skin image in a skin color model based on the training dataset. Here in this paper, we present the survey of the skin pixel segmentation using the learning algorithms.
3 citations
Cites background from "Image Segmentation and Retrievals b..."
...[1] G.V.S. Raj Kumar, K.SrinivasaRao and P. Srinivasa Rao (2011), “Image Segmentation and Retrievals based on finite doubly truncated bivariate Gaussian mixture model and K-means”, International Journal of Computer Applications, Volume 25....
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Journal Article•
TL;DR: The skin colour is modeled by a finite bivariate Pearsonian type-IVa mixture distribution under HSI colour space of the image and the proposed segmentation algorithm performs better with respect to the segmentation quality metrics like PRI, GCE and VOI.
Abstract: The human computer interaction with respect to skin colour is an important area of research due to its ready applications in several areas like face recognition, surveillance, image retrievals, identification, gesture analysis, human tracking etc. For efficient skin colour segmentation statistical modeling is a prime desiderata. In general skin colour segment is done based on Gaussian mixture model. Due to the limitations on GMM like symmetric and mesokurtic nature the accuracy of the skin colour segmentation is affected. To improve the accuracy of the skin colour segmentation system, In this paper the skin colour is modeled by a finite bivariate Pearsonian type-IVa mixture distribution under HSI colour space of the image. The model parameters are estimated by EM algorithm. Using the Bayesian frame the segmentation algorithm is proposed. Through experimentation it is observed that the proposed skin colour segmentation algorithm perform better with respect to the segmentation quality metrics like PRI, GCE and VOI. The ROC curves plotted for the system also revealed that the developed algorithm segment pixels in the image more efficiently. Keywords : Skin colour segmentation, HSI colour space, Bivariate Pearson type IVa mixture model, Image segmentation metrics.
2 citations
12 Apr 2019
2 citations
References
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TL;DR: This work formulate the interaction between image segmentation and object recognition in the framework of the Expectation-Maximization (EM) algorithm as the assignment of image observations to object hypotheses and phrase it as the E-step, while the M-step amounts to fitting the object models to the observations.
Abstract: In this work, we formulate the interaction between image segmentation and object recognition in the framework of the Expectation-Maximization (EM) algorithm. We consider segmentation as the assignment of image observations to object hypotheses and phrase it as the E-step, while the M-step amounts to fitting the object models to the observations. These two tasks are performed iteratively, thereby simultaneously segmenting an image and reconstructing it in terms of objects. We model objects using Active Appearance Models (AAMs) as they capture both shape and appearance variation. During the E-step, the fidelity of the AAM predictions to the image is used to decide about assigning observations to the object. For this, we propose two top-down segmentation algorithms. The first starts with an oversegmentation of the image and then softly assigns image segments to objects, as in the common setting of EM. The second uses curve evolution to minimize a criterion derived from the variational interpretation of EM and introduces AAMs as shape priors. For the M-step, we derive AAM fitting equations that accommodate segmentation information, thereby allowing for the automated treatment of occlusions. Apart from top-down segmentation results, we provide systematic experiments on object detection that validate the merits of our joint segmentation and recognition approach.
52 citations
"Image Segmentation and Retrievals b..." refers methods in this paper
...Image analysis techniques can be classified into two major groups: 1) Statistical, which uses probability distribution functions of pixels and regions to characterize the image (Dubes R.C. and Jain A.K. (1989)), and 2) Structural, which analyzes the image in terms of organization and relationship…...
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18 Aug 2009
TL;DR: A color high-resolution, non-uniform quantized color histograms is proposed and the improving representation about histogram is proposed too and major color, major segmentation block, and a new Gray scale co-existing matrix’s method are proposed.
Abstract: The distribution of pixel colors in an image generally contains interesting information. Recently, many researchers have analyzed the color attributes of an image and used it as the features of the images for querying [1,2,3]. Color histogram [1, 2, 3] is one of the most frequently used image features in the field of color-based image retrieval. The color histogram is widely used as an important color feature indicating the contents of the images in content-based image retrieval (CBIR) [4][5] systems. Specifically histogram-based algorithms are considered to be effective for color image indexing. Color histogram describes the global distribution of pixels of an image which is insensitive to variations in scale and easy to calculate. However, the high-resolution color histograms are usually high dimension and contain much redundant information which does not relate to the image contents, while the low-resolution histograms can not provide adequate discriminative information for image classification. And an image often includes a part of colors but not all, So there will be many accounts of colors are zeros. In order to save space, we shouldn’t need store them. In this paper, a color high-resolution, non-uniform quantized color histogram is proposed and the improving representation about histogram is proposed too. Major color, major segmentation block, and a new Gray scale co-existing matrix’s method are proposed.
46 citations
"Image Segmentation and Retrievals b..." refers background in this paper
...…can be classified into two major groups: 1) Statistical, which uses probability distribution functions of pixels and regions to characterize the image (Dubes R.C. and Jain A.K. (1989)), and 2) Structural, which analyzes the image in terms of organization and relationship of pixels and regions…...
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TL;DR: Theoretical and experimental results indicate that, for images of moderate quality, the detection operation is robust, the parameter estimates are accurate, and the segmentation errors are small.
Abstract: Finite normal mixture (FNM) model-based image segmentation techniques adopt the following detection-estimation-classification paradigm: (1) detect the number of image regions by using theoretical information criteria; (2) estimate model parameters by using expectation-maximization (EM)/classification-maximization (CM) algorithms; and (3) classify pixels into regions by using various classifiers. This paper presents a theoretical framework to evaluate the performance of this class of image segmentation techniques. For the detection performance, probabilities of over-detection and under-detection of the number of image regions are defined, and the associated formulae in terms of model parameters and image quality are derived. For the estimation performance, both EM and CM algorithms are showed to produce asymptotically unbiased ML estimates of model parameters in the case of no-overlap. Cramer-Rao bounds of variances of these estimates are derived. For the classification performance, misclassification probability for the Bayesian classifier is defined, and a simple formula based on parameter estimates and classified data is derived to evaluate segmentation errors. This evaluation method provides both theoretically approachable accuracy limits of the techniques and practically achievable performance of the given images. Theoretical and experimental results are in good agreement and indicate that, for images of moderate quality, the detection operation is robust, the parameter estimates are accurate, and the segmentation errors are small.
42 citations
10 May 2010
TL;DR: The results show that the proposed segmentation technique based on HSI has successfully segmented the acute leukemia images while preserving significant features and removing background noise.
Abstract: The Image segmentation plays an important role in computer vision and image processing areas. In this paper, the use of color segmentation for segmenting acute leukemia images is proposed. The segmentation technique segments each leukemia image into two regions: blast and background. In our approach, the segmentation is based on HSI and RGB color space. The performance comparison between the segmentation algorithms based on HSI and RGB color space is carried out to choose a better color image segmentation for blast detection. The results show that the proposed segmentation technique based on HSI has successfully segmented the acute leukemia images while preserving significant features and removing background noise.
38 citations
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...2 1 1 2 2 1 1 2 2 2 1 2 1 2 a b b b c c and c = 1/ 2 21 , , are the ordinate and area of a standard Normal distribution....
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...( , )f x y is the probability density function of the bivariate Normal distribution given in equation(1)....
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16 Apr 2010
TL;DR: This paper proposes a fuzzy clustering application into image segmentation, a vital element of model identification field that means distinguishing and classifying things that are provided with similar properties.
Abstract: Image segmentation is the basis of image analysis & understanding, and a crucial part and an oldest and hardest problem of image processing. Clustering, a vital element of model identification field means distinguishing and classifying things that are provided with similar properties. As it so happens that the problem of image segmentation is exactly the problem of classifying pixel set of image, clustering analysis is naturally applied into image segmentation. Based on image segmentation and model identification technologies and considering application characteristics of clustering method into image segmentation, this paper proposes a fuzzy clustering application into image segmentation.
13 citations
"Image Segmentation and Retrievals b..." refers methods in this paper
...Image analysis techniques can be classified into two major groups: 1) Statistical, which uses probability distribution functions of pixels and regions to characterize the image (Dubes R.C. and Jain A.K. (1989)), and 2) Structural, which analyzes the image in terms of organization and relationship…...
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