This paper focuses on the problem Image Segmentation which aims at sub dividing a given image into its constituent objects. Here an unsupervised method for color image segmentation is proposed where we first perform a Minimum Spanning Tree (MST) based “natural grouping” of the image pixels to find out the clusters of the pixels having RGB values within a certain range present in the image. Then the pixels nearest to the centers of those clusters are found out and marked as the seeds. They are then used for region growing based image segmentation purpose. After that a region merging based segmentation method having a suitable threshold is performed to eliminate the effect of over segmentation that may still persist after the region growing method. This proposed method is unsupervised as it does not require any prior information about the number of regions present in a given image. The experimental results show that the proposed method can find homogeneous regions present in a given image efficiently.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Survey of Image Segmentation based on Artificial Intelligence and Evolution...IOSR Journals
Abstract : In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to
the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process
by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous
and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest
domain-independent abstraction of an input image. Image segmentation is an important processing step in many
image, video and computer vision applications. Extensive research has been done in creating many different
approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm
produces more accurate segmentations than another, whether it be for a particular image or set of images, or
more generally, for a whole class of images.
In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach
methods that have been proposed in the literature. The rest of the paper is organized as follows. 1.
Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed
Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.
Keywords: Image Segmentation, Segmentation Algorithm, Artificial Intelligence, Evolutionary Algorithm,
Neural Network, Fuzzy Set, Clustering.
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of two steps: enhancement and segmentation. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We applied mathematical morphology to increase the contrast in MRI images and to segment MRI images. Some of experimental results on brain images show the feasibility and the performance of the proposed approach.
Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clus...CSCJournals
Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process [5] Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Survey of Image Segmentation based on Artificial Intelligence and Evolution...IOSR Journals
Abstract : In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to
the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process
by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous
and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest
domain-independent abstraction of an input image. Image segmentation is an important processing step in many
image, video and computer vision applications. Extensive research has been done in creating many different
approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm
produces more accurate segmentations than another, whether it be for a particular image or set of images, or
more generally, for a whole class of images.
In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach
methods that have been proposed in the literature. The rest of the paper is organized as follows. 1.
Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed
Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.
Keywords: Image Segmentation, Segmentation Algorithm, Artificial Intelligence, Evolutionary Algorithm,
Neural Network, Fuzzy Set, Clustering.
Segmentation of Tumor Region in MRI Images of Brain using Mathematical Morpho...CSCJournals
This paper introduces an efficient detection of brain tumor from cerebral MRI images. The methodology consists of two steps: enhancement and segmentation. To improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase an enhancement process is applied. We applied mathematical morphology to increase the contrast in MRI images and to segment MRI images. Some of experimental results on brain images show the feasibility and the performance of the proposed approach.
Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clus...CSCJournals
Segmentation of images holds an important position in the area of image processing. It becomes more important while typically dealing with medical images where pre-surgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process [5] Computer aided detection of abnormal growth of tissues is primarily motivated by the necessity of achieving maximum possible accuracy. Manual segmentation of these abnormal tissues cannot be compared with modern day’s high speed computing machines which enable us to visually observe the volume and location of unwanted tissues. A well known segmentation problem within MRI is the task of labeling voxels according to their tissue type which include White Matter (WM), Grey Matter (GM) , Cerebrospinal Fluid (CSF) and sometimes pathological tissues like tumor etc. This paper describes an efficient method for automatic brain tumor segmentation for the extraction of tumor tissues from MR images. It combines Perona and Malik anisotropic diffusion model for image enhancement and Kmeans clustering technique for grouping tissues belonging to a specific group. The proposed method uses T1, T2 and PD weighted gray level intensity images. The proposed technique produced appreciative results
A NOVEL PROBABILISTIC BASED IMAGE SEGMENTATION MODEL FOR REALTIME HUMAN ACTIV...sipij
Automatic human activity detection is one of the difficult tasks in image segmentation application due to
variations in size, type, shape and location of objects. In the traditional probabilistic graphical
segmentation models, intra and inter region segments may affect the overall segmentation accuracy. Also,
both directed and undirected graphical models such as Markov model, conditional random field have
limitations towards the human activity prediction and heterogeneous relationships. In this paper, we have
studied and proposed a natural solution for automatic human activity segmentation using the enhanced
probabilistic chain graphical model. This system has three main phases, namely activity pre-processing,
iterative threshold based image enhancement and chain graph segmentation algorithm. Experimental
results show that proposed system efficiently detects the human activities at different levels of the action
datasets.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Mammogram image segmentation using rough clusteringeSAT Journals
Abstract The mammography is the most effective procedure to diagnosis the breast cancer at an early stage. This paper proposes mammogram image segmentation using Rough K-Means (RKM) clustering algorithm. The median filter is used for pre-processing of image and it is normally used to reduce noise in an image. The 14 Haralick features are extracted from mammogram image using Gray Level Co-occurrence Matrix (GLCM) for different angles. The features are clustered by K-Means, Fuzzy C-Means (FCM) and Rough K-Means algorithms to segment the region of interests for classification. The result of the segmentation algorithms compared and analyzed using Mean Square Error (MSE) and Root Means Square Error (RMSE). It is observed that the proposed method produces better results that the existing methods. Keywords— Mammogram, Data mining, Image Processing, Feature Extraction, Rough K- Means and Image Segmentation
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
A Methodology for Extracting Standing Human Bodies from Single Imagesjournal ijrtem
Abstract: Extraction of the image of human body in unconstrained still images is challenging due to several factors, including shading, image noise, occlusions, background clutter, the high degree of human body deformability, and the unrestricted positions due to in and out of the image plane rotations. we propose a bottom-up approach for human body segmentation in static images. We decompose the problem into three sequential problems: Face detection, upper body extraction, and lower body extraction, since there is a direct pair wise correlation among them. Index Terms: Skin segmentation, Torso, Face recognition, Thresholding, Ethnicity, Morphology.
Multitude Regional Texture Extraction for Efficient Medical Image Segmentationinventionjournals
Image processing plays a major role in evaluation of images in many concerns. Manual interpretation of the image is time consuming process and it is susceptible to human errors. Computer assisted approaches for analyzing the images have increased in latest evolution of image processing. Also it has highlighted its performance more in the field of medical sciences. Many techniques are available for the involvement in processing of images, evaluation, extraction etc. The main goal of image segmentation is cluster pixeling the regions corresponding to individual surfaces, objects, or natural parts of objects and to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. The proposed method is to conquer segmentation and texture extraction with Regional and Multitude and techniques involved in it. Ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted brain segmentation and disease diagnosis applications.First for ultra sound we present region based segmentation.Homogeneous regions depends on image granularity features. Second a local threshold based multitude texture regional seed segmentation for medical image segmentation is proposed. Here extraction is done with dimensions comparable to the speckle size are to be extracted. The algorithm provides a less medical metrics awareness in a minimum user interaction environment. The shape and size of the growing regions depend on look up table entries.
PERFORMANCE ANALYSIS OF CLUSTERING BASED IMAGE SEGMENTATION AND OPTIMIZATION ...cscpconf
Partitioning of an image into several constituent components is called image segmentation.
Myriad algorithms using different methods have been proposed for image segmentation. Many
clustering algorithms and optimization techniques are also being used for segmentation of
images. A major challenge in segmentation evaluation comes from the fundamental conflict
between generality and objectivity. As there is a glut of image segmentation techniques
available today, customer who is the real user of these techniques may get obfuscated. In this
paper to address the above described problem some image segmentation techniques are evaluated based on their consistency in different applications. Based on the parameters used quantification of different clustering algorithms is done.
Towards Semantic Clustering – A Brief OverviewCSCJournals
Image clustering is an important technology which helps users to get hold of the large amount of online visual information, especially after the rapid growth of the Web. This paper focuses on image clustering methods and their application in image collection or online image repository. Current progress of image clustering related to image retrieval and image annotation are summarized and some open problems are discussed. Related works are summarized based on the problems addressed, which are image segmentation, compact representation of image set, search space reduction, and semantic gap. Issues are also identified in current progress and semantic clustering is conjectured to be the potential trend. Our framework of semantic clustering as well as the main abstraction levels involved is briefly discussed.
Performance Evaluation Of Ontology And Fuzzybase Cbiracijjournal
In This Paper, We Have Done Performance Evaluation Of Ontology Using Low-Level Features Like
Color, Texture And Shape Based Cbir, With Topic Specific Cbir.The Resulting Ontology Can Be Used
To Extract The Appropriate Images From The Image Database. Retrieving Appropriate Images From An
Image Database Is One Of The Difficult Tasks In Multimedia Technology. Our Results Show That The
Values Of Recall And Precision Can Be Enhanced And This Also Shows That Semantic Gap Can Also Be
Reduced. The Proposed Algorithm Also Extracts The Texture Values From The Images Automatically
With Also Its Category (Like Smooth, Course Etc) As Well As Its Technical Interpretation
MAGNETIC RESONANCE BRAIN IMAGE SEGMENTATIONVLSICS Design
Segmentation of tissues and structures from medical images is the first step in many image analysis applications developed for medical diagnosis. With the growing research on medical image segmentation, it is essential to categorize the research outcomes and provide researchers with an overview of the existing segmentation techniques in medical images. In this paper, different image segmentation methods applied on magnetic resonance brain images are reviewed. The selection of methods includes sources from image processing journals, conferences, books, dissertations and thesis. The conceptual details of the methods are explained and mathematical details are avoided for simplicity. Both broad and detailed categorizations of reviewed segmentation techniques are provided. The state of art research is provided with emphasis on developed techniques and image properties used by them. The methods defined are not always mutually independent. Hence, their inter relationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
Color Image Segmentation based on JND Color HistogramCSCJournals
This paper proposes a new color image segmentation approach based on JND (Just Noticeable Difference) histogram. Histogram of the given color image is computed using JND color model. This samples each of the three axes of color space so that just enough number of visually different color bins (each bin containing visually similar colors) are obtained without compromising the visual image content. The histogram bins are further reduced using agglomeration process. This merges similar histogram bins together based on a specific threshold in terms of JND. This agglomerated histogram yields the final segmentation based on similar colors. The performance of the proposed approach is evaluated on Berkeley Segmentation Database. Two significant criterias namely PSNR and PRI (Probabilistic Rand Index) are used to evaluate the performance. Experimental results show that the proposed approach gives better results than conventional color histogram (CCH) based method and with drastically reduced time complexity.
A NOVEL PROBABILISTIC BASED IMAGE SEGMENTATION MODEL FOR REALTIME HUMAN ACTIV...sipij
Automatic human activity detection is one of the difficult tasks in image segmentation application due to
variations in size, type, shape and location of objects. In the traditional probabilistic graphical
segmentation models, intra and inter region segments may affect the overall segmentation accuracy. Also,
both directed and undirected graphical models such as Markov model, conditional random field have
limitations towards the human activity prediction and heterogeneous relationships. In this paper, we have
studied and proposed a natural solution for automatic human activity segmentation using the enhanced
probabilistic chain graphical model. This system has three main phases, namely activity pre-processing,
iterative threshold based image enhancement and chain graph segmentation algorithm. Experimental
results show that proposed system efficiently detects the human activities at different levels of the action
datasets.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
MEDICAL IMAGE TEXTURE SEGMENTATION USINGRANGE FILTERcscpconf
Medical image segmentation is a frequent processing step in image understanding and computer
aided diagnosis. In this paper, we propose medical image texture segmentation using texture
filter. Three different image enhancement techniques are utilized to remove strong speckle noise as well enhance the weak boundaries of medical images. We propose to exploit the concept of range filtering to extract the texture content of medical image. Experiment is conducted on ImageCLEF2010 database. Results show the efficacy of our proposed medical image texture segmentation.
Mammogram image segmentation using rough clusteringeSAT Journals
Abstract The mammography is the most effective procedure to diagnosis the breast cancer at an early stage. This paper proposes mammogram image segmentation using Rough K-Means (RKM) clustering algorithm. The median filter is used for pre-processing of image and it is normally used to reduce noise in an image. The 14 Haralick features are extracted from mammogram image using Gray Level Co-occurrence Matrix (GLCM) for different angles. The features are clustered by K-Means, Fuzzy C-Means (FCM) and Rough K-Means algorithms to segment the region of interests for classification. The result of the segmentation algorithms compared and analyzed using Mean Square Error (MSE) and Root Means Square Error (RMSE). It is observed that the proposed method produces better results that the existing methods. Keywords— Mammogram, Data mining, Image Processing, Feature Extraction, Rough K- Means and Image Segmentation
MRIIMAGE SEGMENTATION USING LEVEL SET METHOD AND IMPLEMENT AN MEDICAL DIAGNOS...cseij
Image segmentation plays a vital role in image processing over the last few years. The goal of image segmentation is to cluster the pixels into salient image regions i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper, we propose a medical diagnosis system by using level set method for segmenting the MRI image which investigates a new variational level set algorithm without re- initialization to segment the MRI image and to implement a competent medical diagnosis system by using MATLAB. Here we have used the speed function and the signed distance function of the image in segmentation algorithm. This system consists of thresholding technique, curve evolution technique and an eroding technique. Our proposed system was tested on some MRI Brain images, giving promising results by detecting the normal or abnormal condition specially the existence of tumers. This system will be applied to both simulated and real images with promising results
A Methodology for Extracting Standing Human Bodies from Single Imagesjournal ijrtem
Abstract: Extraction of the image of human body in unconstrained still images is challenging due to several factors, including shading, image noise, occlusions, background clutter, the high degree of human body deformability, and the unrestricted positions due to in and out of the image plane rotations. we propose a bottom-up approach for human body segmentation in static images. We decompose the problem into three sequential problems: Face detection, upper body extraction, and lower body extraction, since there is a direct pair wise correlation among them. Index Terms: Skin segmentation, Torso, Face recognition, Thresholding, Ethnicity, Morphology.
Multitude Regional Texture Extraction for Efficient Medical Image Segmentationinventionjournals
Image processing plays a major role in evaluation of images in many concerns. Manual interpretation of the image is time consuming process and it is susceptible to human errors. Computer assisted approaches for analyzing the images have increased in latest evolution of image processing. Also it has highlighted its performance more in the field of medical sciences. Many techniques are available for the involvement in processing of images, evaluation, extraction etc. The main goal of image segmentation is cluster pixeling the regions corresponding to individual surfaces, objects, or natural parts of objects and to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. The proposed method is to conquer segmentation and texture extraction with Regional and Multitude and techniques involved in it. Ultrasound (US) is increasingly considered as a viable alternative imaging modality in computer-assisted brain segmentation and disease diagnosis applications.First for ultra sound we present region based segmentation.Homogeneous regions depends on image granularity features. Second a local threshold based multitude texture regional seed segmentation for medical image segmentation is proposed. Here extraction is done with dimensions comparable to the speckle size are to be extracted. The algorithm provides a less medical metrics awareness in a minimum user interaction environment. The shape and size of the growing regions depend on look up table entries.
PERFORMANCE ANALYSIS OF CLUSTERING BASED IMAGE SEGMENTATION AND OPTIMIZATION ...cscpconf
Partitioning of an image into several constituent components is called image segmentation.
Myriad algorithms using different methods have been proposed for image segmentation. Many
clustering algorithms and optimization techniques are also being used for segmentation of
images. A major challenge in segmentation evaluation comes from the fundamental conflict
between generality and objectivity. As there is a glut of image segmentation techniques
available today, customer who is the real user of these techniques may get obfuscated. In this
paper to address the above described problem some image segmentation techniques are evaluated based on their consistency in different applications. Based on the parameters used quantification of different clustering algorithms is done.
Towards Semantic Clustering – A Brief OverviewCSCJournals
Image clustering is an important technology which helps users to get hold of the large amount of online visual information, especially after the rapid growth of the Web. This paper focuses on image clustering methods and their application in image collection or online image repository. Current progress of image clustering related to image retrieval and image annotation are summarized and some open problems are discussed. Related works are summarized based on the problems addressed, which are image segmentation, compact representation of image set, search space reduction, and semantic gap. Issues are also identified in current progress and semantic clustering is conjectured to be the potential trend. Our framework of semantic clustering as well as the main abstraction levels involved is briefly discussed.
Performance Evaluation Of Ontology And Fuzzybase Cbiracijjournal
In This Paper, We Have Done Performance Evaluation Of Ontology Using Low-Level Features Like
Color, Texture And Shape Based Cbir, With Topic Specific Cbir.The Resulting Ontology Can Be Used
To Extract The Appropriate Images From The Image Database. Retrieving Appropriate Images From An
Image Database Is One Of The Difficult Tasks In Multimedia Technology. Our Results Show That The
Values Of Recall And Precision Can Be Enhanced And This Also Shows That Semantic Gap Can Also Be
Reduced. The Proposed Algorithm Also Extracts The Texture Values From The Images Automatically
With Also Its Category (Like Smooth, Course Etc) As Well As Its Technical Interpretation
MAGNETIC RESONANCE BRAIN IMAGE SEGMENTATIONVLSICS Design
Segmentation of tissues and structures from medical images is the first step in many image analysis applications developed for medical diagnosis. With the growing research on medical image segmentation, it is essential to categorize the research outcomes and provide researchers with an overview of the existing segmentation techniques in medical images. In this paper, different image segmentation methods applied on magnetic resonance brain images are reviewed. The selection of methods includes sources from image processing journals, conferences, books, dissertations and thesis. The conceptual details of the methods are explained and mathematical details are avoided for simplicity. Both broad and detailed categorizations of reviewed segmentation techniques are provided. The state of art research is provided with emphasis on developed techniques and image properties used by them. The methods defined are not always mutually independent. Hence, their inter relationships are also stated. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in application.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
Color Image Segmentation based on JND Color HistogramCSCJournals
This paper proposes a new color image segmentation approach based on JND (Just Noticeable Difference) histogram. Histogram of the given color image is computed using JND color model. This samples each of the three axes of color space so that just enough number of visually different color bins (each bin containing visually similar colors) are obtained without compromising the visual image content. The histogram bins are further reduced using agglomeration process. This merges similar histogram bins together based on a specific threshold in terms of JND. This agglomerated histogram yields the final segmentation based on similar colors. The performance of the proposed approach is evaluated on Berkeley Segmentation Database. Two significant criterias namely PSNR and PRI (Probabilistic Rand Index) are used to evaluate the performance. Experimental results show that the proposed approach gives better results than conventional color histogram (CCH) based method and with drastically reduced time complexity.
Color based image processing , tracking and automation using matlabKamal Pradhan
Image processing is a form of signal processing in which the input is an image, such as a photograph or video frame. The output of image processing may be either an image or, a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. This project aims at processing the real time images captured by a Webcam for motion detection and Color Recognition and system automation using MATLAB programming.
In color based image processing we work with colors instead of object. Color provides powerful information for object recognition. A simple and effective recognition scheme is to represent and match images on the basis of color histograms.
Tracking refers to detection of the path of the color once the color based processing is done the color becomes the object to be tracked this can be very helpful in security purposes.
Automation refers to an automated system is any system that does not require human intervention. In this project I’ve automated the mouse that work with our gesture and do the desired tasks.
At the end of this lesson, you should be able to;
identify color formation and how color visualize.
describe primary and secondary colors.
describe display on CRT and LCD.
comprehend RGB, CMY, CMYK and HSI color models.
Color image processing challenges zewail city workshop 7 march 2015DrNoura Semary
Life around us is full of color. It was not created colorful for just enjoyed its beauty, but there are many sciences and applications that rely on different colors to analyze things. In information technology, color images processing is still a challenge. Automated intelligent systems are needed for most of important areas of scientific research and treatment.
Whereas numerous applications and objectives and techniques of this science confine ourselves only display some of the research that been interested in the previous period. In an attempt to re-coloring old films, which is a technical and cultural heritage of our country, we were able to put our hands on the most important technologies and challenges in this regard, and we were able to design a system that works on coloring old movies efficiently. As the colors has aesthetic importance , also it’s found that the images of gray important technology in terms of size so we proposed several techniques for compressing color images depending on the idea of hiding colors. While we seek to improve our colorful vision, there is a community of at least 4 million people cannot enjoy these colors because of a sort of color vision deficiency, or the so-called color blindness. So we study the situation and the challenges of the problem and prove the alleged ratios using random sample and we can prove that technology can be used to assist diagnosis and improving the vision of these cases without relying on medicine. For totally blind peoples, We also discussed how we can help these blind in the sense of color and graphics where there is as yet no system is interested in this issue, which is very important especially for people with acquired disabilities. Using spectral cameras also has many applications and challenges, especially in the field of agriculture, medicine, remote sensing ..
Review of Image Segmentation Techniques based on Region Merging ApproachEditor IJMTER
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 image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A Survey on Image Segmentation and its Applications in Image Processing IJEEE
As technology grows day by day computer vision becomes a vital field of understanding the behavior of an image. Image segmentation is a sub field of computer vision that deals with the partition of objects into number of segments. Image segmentation found a huge application in pattern reorganization, texture analysis as well as in medial image processing. This paper focus on distinct sort of image segmentation techniques that are utilized in computer vision. Thus a survey has been created for various image segmentation techniques that describe the importance of the same. Comparison and conclusion has been created within the finish of this paper.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
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Color Image Segmentation Technique Using “Natural Grouping” of Pixels
1. Biplab Banerjee, Tanusree Bhattacharjee & Nirmalya Chowdhury
International Journal of Image Processing(IJIP), Volume(4): Issue(4) 320
Color Image Segmentation Technique Using “Natural Grouping”
of Pixels
Biplab Banerjee gettbiplab@gmail.com
Computer Science and Engg. Dept
Jadavpur University Kolkata –
700032, India
Tanusree Bhattacharjee tanusree_bhattacharjee85@yahoo.co.in
Computer Science and Engg. Dept
Jadavpur University Kolkata –
700032, India
Nirmalya Chowdhury nirmalya_chowdhury@yahoo.com
Faculty,Computer Science and Engg. Dept
Jadavpur University Kolkata –
700032, India
Abstract
This paper focuses on the problem Image Segmentation which aims at sub
dividing a given image into its constituent objects. Here an unsupervised method
for color image segmentation is proposed where we first perform a Minimum
Spanning Tree (MST) based “natural grouping” of the image pixels to find out the
clusters of the pixels having RGB values within a certain range present in the
image. Then the pixels nearest to the centers of those clusters are found out and
marked as the seeds. They are then used for region growing based image
segmentation purpose. After that a region merging based segmentation method
having a suitable threshold is performed to eliminate the effect of over
segmentation that may still persist after the region growing method. This
proposed method is unsupervised as it does not require any prior information
about the number of regions present in a given image. The experimental results
show that the proposed method can find homogeneous regions present in a
given image efficiently.
Keywords: Segmentation, Region Growing, Natural Grouping.
1. INTRODUCTION
Segmentation of an image entails the division or separation of the image into regions of similar
attributes. In another way, image segmentation is nothing but pixel classification. The level to
which the segmentation process is to be carried out depends on the particular problem being
solved. It is considered as an important operation for meaningful analysis and interpretation of the
images acquired. It is one of the most critical components of an image analysis and/or pattern
recognition system and still is considered as one of the most challenging tasks in the field of
2. Biplab Banerjee, Tanusree Bhattacharjee & Nirmalya Chowdhury
International Journal of Image Processing(IJIP), Volume(4): Issue(4) 321
image processing. It has applications in several domains like Medical Science, Analysis of
Remotely Sensed Image, Fingerprint Recognition, and Traffic System Monitoring and so on.
Image segmentation algorithms are generally based on one of two basic properties of intensity
values of the image pixels: discontinuity and similarity. In the first category, the approach is to
partition an image based on abrupt changes in intensity values. Edge detection techniques fall in
this category which is similar to boundary extraction. On the other hand, in the second category,
the idea is to partition the image into different regions such that pixels belonging to a given region
are similar with respect to a set of predefined criteria’s. Researchers have been working on these
two directions for years and have proposed various methods keeping those region based
properties in mind. It should be noted that there is no fixed approach for segmentation. Based on
the similarity or discontinuity criteria, many image segmentation methods have been proposed
which can be broadly classified into six groups: (1) Edge Detection, (2) Histogram based method,
(3) Clustering (K-Means clustering, Fuzzy C-means clustering etc.), (4) Region based methods
(Region growing, Region splitting & merging), (5) Physical Model based approach,(6)Neural
Network based segmentation methods [20].
The edge detection method is one of the widely used approaches to the problem of image
segmentation. It is based on the detection of points with abrupt changes at gray levels. The main
disadvantages of the edge detection technique are that it does not work well when images have
many edges because in that case the segmentation method produces an over segmented result,
and it cannot easily identify a closed curve or boundary. For an edge based method to be
efficient, it should detect the global edges and the edges have to be continuous.
Histogram-based methods are very efficient in terms of time complexity when compared to other
image segmentation methods because they typically require only one pass through the pixels. In
this technique, a histogram is computed from all of the pixels in the image, and the peaks and
valleys in the histogram are detected. Now the pixels in between two consecutive valleys can be
considered to belong to a single cluster. One of the disadvantages of this method is that it is
unable to work well when the image has no apparent gray level histogram peak. The other
disadvantage is that the continuity of the segmented regions cannot be ensured. For the
histogram based method to be efficient, we should focus on global peaks which are likely to
correspond to the dominant regions in the image.
Clustering methods are also used for image segmentation purpose [4] [5]. The segmentation
method incorporating clustering techniques encounters great difficulties when determining the
number of clusters actually present in the corresponding feature space or extracting the
appropriate feature.
The region based methods are based on the similarity of pixels within a region. Sometimes a
hybrid method incorporating the edge based and region based methods have been proved to be
useful for certain applications. The first region growing method was the seeded region growing
method. This method takes a set of seeds as input along with the image. The seeds mark each of
the objects to be segmented. The regions are iteratively grown by comparing all unallocated
neighboring pixels to the regions. The difference between a pixel's intensity value and the
region's mean, δ, can be used as a measure of similarity. The pixel with the smallest difference
measured this way is allocated to the respective region. This process continues until all pixels are
allocated to a region. Seeded region growing requires seeds as additional input. The
segmentation results are dependent on the choice of seeds. Noise in the image can cause the
seeds to be poorly placed. Region splitting & merging is a modified algorithm that doesn't require
explicit seeds. It starts off with a single region first, which is the image as a whole. Then the
region is splited into four different sub region based on some dissimilarity measures to construct a
quad tree structure of regions. The regions are splited to the extreme level so that no more
regions splitting can be done now. Then the splited regions are merged iteratively to have the
final segmentation.
3. Biplab Banerjee, Tanusree Bhattacharjee & Nirmalya Chowdhury
International Journal of Image Processing(IJIP), Volume(4): Issue(4) 322
The model based image segmentation assumes that given an image, its individual regions follow
a repetitive form of geometrical structure. This is segmentation using texture feature.
Neural Network segmentation relies on processing small areas of an image using an artificial
neural network or a set of different neural networks. After such processing the decision-making
mechanism marks the areas of an image accordingly to the category recognized by the neural
network. A type of network designed especially for this is the Kohonen self-organizing map.
The proposed technique is based on the region based method for image segmentation. In
general, the region based methods suffer from the problem of selection of initial seeds. We have
formalized a method to sort out this problem. The method proposed in this paper uses the
concept of “natural grouping” [10] based on the three color channels to find the seed pixels.
Hence each seed pixel acts as a pivot for a region of the given image. Then we start the process
of region growing from each such seed pixel. After the completion of this region growing process,
we obtain as many regions as the number of seed pixels. The region growing stage is followed by
a region merging method which reduces the effect of over segmentation to a great extent.
The rest of the paper is organized as follows, section 2 discusses about some existing works on
image segmentation, then in section 3 a problem definition is given and in section 4, our method
is proposed then in section 5, some experimental results are shown and a comparison is done
with some other well-known segmentation techniques to show the effectiveness of our method.
Section 6 deals with the concluding discussion and future improvements that can be done.
2. A BRIEF OVERVIEW OF THE RELATED WORK
Color image segmentation has always been a challenging task for the researchers over the
years. Pal & Pal [12] provided a detail review on various segmentation techniques. Among
myriads of existing image segmentation techniques, many can be considered as unsupervised
clustering methods. For example, region merging is the analogue of agglomerative clustering [1].
Graph cut methods such as minimal cut and normalized cut represent the clustering problem in a
graph theoretic manner [2]. A major problem known as the problem of validity for this kind of
methods is how to determine the number of clusters in an image. Since the problem is largely
unresolved, most techniques need the user to provide a terminating criterion. B.Sowmya and
B.Sheelarani segmented color image using soft computing techniques [3]. The soft computing
techniques they used were Possibilistic C means algorithm (PCM) and competitive neural
network. Fuzzy set and Fussy logic techniques are also used by researchers for solving
segmentation problem. A. Borji et al. described a CLPSO-based Fuzzy Color Image
Segmentation [6]. H. D. Cheng et al. segmented color image on the basis of fuzzy homogeneity
approach [7]. Besides this, artificial neural network (ANN) and Genetic algorithm (GA) techniques
also have been used for image segmentation [8]. There are numerous segmentation techniques
in medical imaging depending on the region of interest. There are atlas-guided techniques and
region growing segmentation methods. Some of them use a semi-automatic approach and still
need some operator interaction. Others are fully automatic and the operator has only a
verification role. Fan et al. proposed a method of automatic image segmentation by integrating
color edge detection and seeded region growing [9]. They used fast Entropy thresholding for
edge extraction. After they obtained color edges, which provided the major geometric structures
in an image, the centroids between these adjacent edge regions were taken as the initial seeds
for seeded region growing. These seeds were then replaced by the centroids of the generated
homogeneous image regions by incorporating the required additional pixels step by step. Adams
and Bischof proposed another method using seeded region growing [13]. Franc Y Shis and
Shouxian Cheng proposed another region based image segmentation method [14] where initial
seeds are selected based on the idea of calculating standard deviation in a neighbor and to check
whether the value is under a threshold and then assigning each pixel in that region as seeds. This
seed selection is followed by a region growing and region merging.
4. Biplab Banerjee, Tanusree Bhattacharjee & Nirmalya Chowdhury
International Journal of Image Processing(IJIP), Volume(4): Issue(4) 323
“Natural grouping” of dataset is a method of identifying the hidden clusters of data points present
in the dataset. For a set of data points, we can draw the scatter diagram of the set. What one
perceives to be the groups present in the set is termed as the natural groups present in the set.
These sorts of algorithms are better than the general clustering algorithms in the fact that, they
don’t need any prior information about the number of clusters present actually in the dataset.
Nirmalya Chowdhury and C. A. Murthy proposed a method of obtaining natural groups present in
a dataset using minimum spanning tree and bayes classifier [10]. Sugar et al. proposed an
information theoretic approach for finding natural groups present in a dataset [11]. Premananda
Jana and Nirmalya Chowdhury proposed another method of natural grouping of a given dataset
using MST of dataset [15].
3. STAEMENT OF THE PROBLEM
Given an image I and homogeneity predicate P, the segmentation of I is a partition of I into K
regions {R1, R2… RK} satisfying the following conditions,
Each pixel in the image should be assigned to a region.
i.e.
1
k
p
p
R I
=
=U
(1)
Each pixel is assigned one and only one region.
i.e. k k kR R∩ = ∅ , 1,2,... ,k kk K k kk∀ = ≠ (2)
Each region satisfies the predicate P, which can be similarity measure in color space or similarity
based on texture or any similarity criteria combining both of them etc.
i.e. P (Rk) = True for k=1, 2 …K (3)
Two different regions cannot satisfy P at the same time.
i.e. ( )k kkP R R∩ =False where k ≠ kk (4)
4. THE PROPOSED METHOD IN THE FORM OF AN ALGORITHM
Fig 1 presents an overview of our algorithm. Here after finding the unique RGB tuples of pixels of
a given image, we perform a MST based natural grouping of the pixels. The pixels closest to the
centers of the groups thus obtained are found out. These pixels serve as the pivot for region
growing image segmentation purpose. This step is immediately followed by a region merging
process. We can divide the algorithm into three parts, namely, seed selection, region growing and
region merging. These steps are discussed in detail in the next section,
4.1 Selection of seed pixels
Seed pixel selection is the initial step for image segmentation based on region growing. The
pixels selected as seeds should satisfy the following properties,
• Every seed should be similar to its neighbors with respect to some criteria P.
• Any pair of seeds should be distinct with respect to the criteria P.
For seed pixel selection purpose, we first perform the natural grouping on the image pixels in the
RGB feature space as RGB space is suitable for color display. In image processing and analysis,
we often transform the <R G B> components into other color spaces. Every color space has its
own advantages and disadvantages. Cheng et. al. compared several color spaces including
RGB, YIQ, YUV, CIE etc. for color image segmentation purpose [16]. For performing natural
grouping, we uses the natural grouping method using minimum spanning tree (MST) proposed by
Premananda Jana and Nirmalya Chowdhury. Using pixels as the vertices of the MST, it has been
used for obtaining the natural groups of pixels based on their RGB values. Euclidean distance
(Equation 5) between the data points has been taken to be the edge weight of the said MST.
5. Biplab Banerjee, Tanusree Bhattacharjee & Nirmalya Chowdhury
International Journal of Image Processing(IJIP), Volume(4): Issue(4) 324
2 2 2
( ) ( ) ( )E i j i j i jD R R G G B B= − + − + − (5)
Then we calculate the sum of the edge weight (ln) of the MST. The threshold for cluster
separation is taken to be,
nl
1
NGTh
n
β ×
=
−
(6)
where n is the no of nodes in the MST [17]. It has been experimentally found that β =2 has
provided consistently good results [15].
Now the edges of the MST are removed for which edge weights exceeds the threshold (6) for
cluster separation. Nodes (data points or pixels here) in each path constitute different clusters of
data points (pixels). The centers of those clusters are found out. Now the image pixels having
intensity values nearest to those cluster centers values are selected as seeds.
FIGURE 1: Outline of the Proposed Method.
4.2 Region growing step
Seed pixels selected by the above method acts as the pivot for each possible region. For
efficiency purpose, every region in the image should contain a seed pixel. But in our proposed
method of seed selection, it may so happen that no seed falls in any of the Region of Interest. We
eliminate this problem effectively in this region growing stage. In the beginning, we are performing
region growing from those initially selected seed pixels. Steps for region growing process are
stated below,
6. Biplab Banerjee, Tanusree Bhattacharjee & Nirmalya Chowdhury
International Journal of Image Processing(IJIP), Volume(4): Issue(4) 325
• For each natural group we compute the Euclidian distance of all data points from the
remaining data points of that cluster using the formula (5). The maximum distance so
obtained is recorded. Then we sort those recorded distances in the increasing order and
chooses the minimum value to be the threshold (ThRG ) for region growing purpose.
• For each seed pixel, we compute the Euclidian distance between the seed pixel and four of
its neighboring pixels.
• If the distances thus found are less than the threshold (ThRG), then they are included in the
region marked by that particular seed.
• If one or more of the neighboring pixels do not satisfy the condition for region growing as
stated above, then they remain unassigned.
• The above methods are repeated for all the seeds.
• For a given seed, the pixels included by region growing method narrated above are labeled
by the color (RGB) of the seed pixel.
• Label all the unassigned pixels by the color of the seed pixel to which they are nearest.
4.3 Region merging step
It is possible that the above mentioned method for region growing may result in generation of
more number of segments than that is actually present in a given image. Thus at this stage, one
needs to use a suitable region merging criteria to merge those regions which should not be given
the identity of separate regions. So to get rid of this over segmentation problem a method of
region merging is employed which is described below,
FIGURE 2: Some Sample Images from the Database Used for Experimentation.
Two regions (R and R’) are merged if,
• They are adjacent.
• If the Euclidian difference between their mean color intensity values is within the threshold
value (ThRM):
2 2
( ) ( ')RMTh b R b R= + (7)
where b(R) is defined as: -
| |1 1
( ) ( )ln
2 | |
RR
b R g
Q R
= ×
× ∂
(8)
where g is the maximum of particular color channel (R or G or B) of the mean color value of
region R. Q is total no of elements in the set of random variable that is used to represent each
color channel. Basically q denotes the level of merging. For our purpose we make use q from 1 to
32 and observe the changes in result. |R| represent the cardinality (no of pixels) of region R. ∂ is
a parameter which is defined as 1/|I|
2
*6 where |I| is the cardinality of the image I.
7. Biplab Banerjee, Tanusree Bhattacharjee & Nirmalya Chowdhury
International Journal of Image Processing(IJIP), Volume(4): Issue(4) 326
The merging threshold is based on the work done by Nock and Nielsen [18] where they segment
images using statistical region merging technique. The above mentioned threshold (ThRM) is
based on the statistical measure having the idea that pixels within a region have the same
expectation value and pixels in different regions have different expectation value.
5. EXPERIMENTAL RESULTS AND DISCUSSIONS
We implemented the algorithm in matlab and tested it on a Pentium Core-2 Duo system with 3
GB RAM. We have performed our experiment on about 100 images collected from the web. Fig. 2
shows some ample images from the database created. fig 3 shows some of the samples from the
collected image database for showing experimental results. The images are color images of
different resolutions. We resized the images into 100 x 100 after starting the segmentation
process. The images are basically scenery images. We have also performed comparison with
some well-known segmentation techniques.
(a) (b) (c)
FIGURE 3: Some Sample Images Collected for Experiment.
(a) (b) (c)
FIGURE 4: Results of the Proposed Method.
(a) (b) (c)
FIGURE 5: Results of the Statistical Region Merging Based Method.
(a) (b) (c)
FIGURE 6: Results of the K-Means Based Method.
Fig 4, Fig 5 and Fig 6 show the comparison of the results obtained by our method with the results
obtained by [18] and [19]. We obtain both the implementation versions of those algorithms from
the web. In fig 4, the results of the segmentation process obtained by the proposed method on
8. Biplab Banerjee, Tanusree Bhattacharjee & Nirmalya Chowdhury
International Journal of Image Processing(IJIP), Volume(4): Issue(4) 327
some sample images collected are depicted. Then the results of [18] are shown in fig 5. The
results show that our method is relatively free from over segmentation problem. This problem is
there in most of the results obtained by the method proposed in [18]. For example, in the result
shown in Fig 5(a), the sky is segmented in three different segments while our method shows in
Fig 4(a) that the sky is segmented into two parts. Besides, the algorithm proposed in [18]
sometimes over merged some regions which make the result difficult to analyze. For example, in
the picture Fig 5(b), the right eye of the man is merged with the eye brow and the region covering
them. But in our method in Fig 4(b), the eye and eye brow can be identified separately. The sky in
Fig 4(c) is easy to understand in comparison with the result in Fig .5(c).
We have made another comparative study with the segmentation algorithm using k-means
clustering proposed by Tse-Wei Chen Yi-Ling Chen Shao-Yi Chien [19]. Fig 6 shows the results.
This algorithm label a segmented region by a randomly selected color value. The results in 6(a)
and 6(c) show that, the sky is over segmented in more number of segments. Whereas the result
in 6(b) shows that, most of the parts are over merged.
Our algorithm lacks in two aspects. It requires an additional scanning of the source image after
the region growing stage. The preprocessing step needed for the natural grouping step, i.e.
drawing the MST requires some addition storage as it requires building the adjacency matrix of
the pixel points. Except these two shortcomings, the algorithm works fine for most of the images.
The time complexity for our algorithm spans three different phases: natural grouping for seed
selection, region growing and region merging. The natural grouping is MST based and we use
Prim’s algorithm for finding the MST. Time complexity of prim’s algorithm is O (E+V) logV where
E is the no of edges and V is the no of vertices present in the MST. It requires O (E) to find the
groups. The time complexity of seed selection is O(c x m x n) where c is the number of clusters
obtained and m x n is the image resolution which is 100 x 100. In region growing, each
unclassified pixel is inserted into the sorted list exactly once. Checking neighboring regions and
calculating distances can be done in constant time. Putting a pixel into the sorted list requires log
(m x n). Therefore, it takes O((m x n) log(m x n)) for region growing. After the completion of the
region growing, we again scan the whole image to label the unassigned pixels. It takes O (m x n)
time.
In region-merging, calculating the differences between regions takes O (r
2
), where r is the number
of regions. To calculate sizes for all the regions, it takes O (m x n). Usually, r is much less than m
x n. To merge two regions, we need to label the pixels in the two regions, calculate the mean for
the new merged region, and calculate the distances between this and other regions. Therefore, it
takes O(m x n) to merge two regions.
6. CONCLUSION
We have presented an efficient algorithm for color image segmentation with automated seed
selection. We performed natural grouping on the image pixels to find out different color bands
present in the image. Experimental results show that our method can produce good results. This
work can be carried on in future by incorporating texture feature along with color feature for
region growing purpose as texture is an important region descriptor.
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