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Journal Article•DOI•

Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and KMeans

G.V.S. Rajkumar, Rao K. Srinivasa, Rao P. Srinivasa
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.

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Citations
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Journal Article•DOI•
Studies on Texture Segmentation Using D- Dimensional Generalized Gaussian Distribution integrated with Hierarchical Clustering

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K. Naveen Kumar1, K. Srinivasa Rao, Yarramalle Srinivas, Ch. Satyanarayana•
GITAM University Hyderabad Campus1
08 Mar 2016-International Journal of Image, Graphics and Signal Processing
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

Cites background from "Image Segmentation and Retrievals b..."

  • ...This also includes Gaussian distribution as a particular case [12]....

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A Robust Skin Colour Segmentation Using Bivariate Pearson Type II

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B. N. Jagadesh, K. Srinivasa Rao, Ch. Satyanarayana
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

Data Mining Based Skin Pixel Detection Applied On Human Images: A Study Paper

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Gagandeep Kaur, Seema Pahwa, Kushagra Sharma
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•
Skin Colour Segmentation using Fintte Bivariate Pearsonian Type-IV a Mixture Model

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K. Srinivasa Rao, B. N. Jagadesh, Ch. Satyanarayana
01 Jan 2012-Computer Engineering and Intelligent Systems
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

Journal Article•DOI•
Registration-based Image Segmentation using Lattice Boltzmann Method

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Yu Chen, Dongxiang Lu
12 Apr 2019

2 citations

References
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Journal Article•DOI•
Image quality measures and their performance

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A.M. Eskicioglu, P.S. Fisher
01 Dec 1995-IEEE Transactions on Communications
TL;DR: Although some numerical measures correlate well with the observers' response for a given compression technique, they are not reliable for an evaluation across different techniques, and a graphical measure called Hosaka plots can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.
Abstract: A number of quality measures are evaluated for gray scale image compression. They are all bivariate, exploiting the differences between corresponding pixels in the original and degraded images. It is shown that although some numerical measures correlate well with the observers' response for a given compression technique, they are not reliable for an evaluation across different techniques. A graphical measure called Hosaka plots, however, can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.

1,660 citations

"Image Segmentation and Retrievals b..." refers background in this paper

  • ...A and B are as given in equations (3) and (4) respectively....

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  • ...where, ( ) i E X and ( ) i E Y are as given in equations (3) and (4) for the i image region....

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  • ...( ) ( ) ( 1) ( ) ( ) 1 2 2 ( ) 1 1 1 ( ) ( ) ( ) , ; , ; , ; ( ) ( ) ( ) ( ) B A 0 l l N N N l l l k s k k k k s k k k l s s s k l l l x y x y x y s s s s s s x t t y t                                   (14) where,   ( ) , ; k l t x y s s  is given in equation (13) , A and B are as given in equations (3) and (4) respectively....

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  • ...c c c                                                                              and c and B are as given in equations (3) and (4) respectively....

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  • ...c c                                                                               and c, A are as given in equation (3) and B is given in equation (4) Since the entire image is a collection of regions, which are characterized by doubly truncated bivariate normal distribution, it can be characterized through a K-component finite doubly truncated bivariate Gaussian distribution and its probability density function is of the form ( , ) ( , / ) 1 K h x y g x y i i i i      (8) where, K is the number of regions, i  >0 are weights such...

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Journal Article•DOI•
Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation

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Jayaram K. Udupa1, Supun Samarasekera1•
University of Pennsylvania1
01 May 1996-Graphical Models and Image Processing
TL;DR: A theory of fuzzy objects forn-dimensional digital spaces based on a notion of fuzzy connectedness of image elements and algorithms for extracting a specified fuzzy object and for identifying all fuzzy objects present in the image data are presented.

925 citations

Journal Article•DOI•
Toward Objective Evaluation of Image Segmentation Algorithms

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Ranjith Unnikrishnan1, Caroline Pantofaru1, Martial Hebert1•
Carnegie Mellon University1
01 Jun 2007-IEEE Transactions on Pattern Analysis and Machine Intelligence
TL;DR: It is demonstrated how a recently proposed measure of similarity, the normalized probabilistic rand (NPR) index, can be used to perform a quantitative comparison between image segmentation algorithms using a hand-labeled set of ground-truth segmentations.
Abstract: Unsupervised image segmentation is an important component in many image understanding algorithms and practical vision systems. However, evaluation of segmentation algorithms thus far has been largely subjective, leaving a system designer to judge the effectiveness of a technique based only on intuition and results in the form of a few example segmented images. This is largely due to image segmentation being an ill-defined problem-there is no unique ground-truth segmentation of an image against which the output of an algorithm may be compared. This paper demonstrates how a recently proposed measure of similarity, the normalized probabilistic rand (NPR) index, can be used to perform a quantitative comparison between image segmentation algorithms using a hand-labeled set of ground-truth segmentations. We show that the measure allows principled comparisons between segmentations created by different algorithms, as well as segmentations on different images. We outline a procedure for algorithm evaluation through an example evaluation of some familiar algorithms - the mean-shift-based algorithm, an efficient graph-based segmentation algorithm, a hybrid algorithm that combines the strengths of both methods, and expectation maximization. Results are presented on the 300 images in the publicly available Berkeley segmentation data set

826 citations

Proceedings Article•DOI•
Color image segmentation

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Yining Deng1, B.S. Manjunath1, Hyun-Doo Shin2•
University of California, Santa Barbara1, Samsung2
23 Jun 1999
TL;DR: In this work, a new approach to fully automatic color image segmentation, called JSEG, is presented, where colors in the image are quantized to several representing classes that can be used to differentiate regions in the photo, thus forming a class-map of the image.
Abstract: In this work, a new approach to fully automatic color image segmentation, called JSEG, is presented. First, colors in the image are quantized to several representing classes that can be used to differentiate regions in the image. Then, image pixel colors are replaced by their corresponding color class labels, thus forming a class-map of the image. A criterion for "good" segmentation using this class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image", in which high and low values correspond to possible region boundaries and region centers, respectively. A region growing method is then used to segment the image based on the multi-scale J-images. Experiments show that JSEG provides good segmentation results on a variety of images.

583 citations

Journal Article•DOI•
Random field models in image analysis

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Richard C. Dubes1, Anil K. Jain1•
Michigan State University1
01 Jan 1989-Journal of Applied Statistics
TL;DR: This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis and allows the introduction of spatial context into pixel labeling problems, such as segmentation and restoration.
Abstract: Image models are useful in quantitatively specifying natural constraints and general assumptions about the physical world and the imaging process. This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis. Random field models permit the introduction of spatial context into pixel labeling problems, such as segmentation and restoration. Random field models also describe textured images and lead to algorithms for generating textured images, classifying textures and segmenting textured images. In spite of some impressive model-based image restoration and texture segmentation results reported in the literature, a number of fundamental issues remain unexplored, such as the specification of MRF models, modeling noise processes, performance evaluation, parameter estimation, the phase transition phenomenon and the comparative analysis of alternative procedures. The literature of random field models is filled with great promise, but...

479 citations

"Image Segmentation and Retrievals b..." refers background in this paper

  • ...                                                                                and c, A are as given in equation (3) The Variance of Y is V(Y) = 2 2  [E (Z2 (2))] – B(2) (2 2  – 1) (6) where, a - a - a - a - b - a 2 2 2 2 2 2 2 2 2 2 2 2 2 E (Z ) ( ) ( ) ( ) - ( ) ( ) - ( ) 2 2 2 2 2 2 2 b - b - a - b 2 2 2 2 2 2 2 2 ( ) ( ) ( ) - ( ) 2 2 2 2 c c...

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  • ...A and B are as given in equations (3) and (4) respectively....

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  • ...1 2 2 1 1 2 2 1 1 1 2 1 2 1 a b b b c c                                                                                                and c is given equation (3) The Variance of X is...

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  • ...where, ( ) i E X and ( ) i E Y are as given in equations (3) and (4) for the i image region....

    [...]

  • ...( ) ( ) ( 1) ( ) ( ) 1 2 2 ( ) 1 1 1 ( ) ( ) ( ) , ; , ; , ; ( ) ( ) ( ) ( ) B A 0 l l N N N l l l k s k k k k s k k k l s s s k l l l x y x y x y s s s s s s x t t y t                                   (14) where,   ( ) , ; k l t x y s s  is given in equation (13) , A and B are as given in equations (3) and (4) respectively....

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Trending Questions (1)
How to Train an image segmentation model?

The experimental results show that this method outperforms the existing model based image segmentation methods.

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