Clustering-Based Color Image Segmentation Using Local Maxima

2018 ◽  
Vol 14 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Kalaivani Anbarasan ◽  
S. Chitrakala
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Automatic Segmentation ◽  
Input Image ◽  
Color Image Segmentation ◽  
Local Maxima ◽  
User Input ◽  
Texture Property ◽  
Multiple Regions

Color image segmentation has contributed significantly to image analysis and retrieval of relevant images. Color image segmentation helps the end user subdivide user input images into unique homogenous regions of similar pixels, based on pixel property. The success of image analysis is largely owing to the reliability of segmentation. The automatic segmentation of a color image into accurate regions without over-segmentation is a tedious task. Our paper focuses on segmenting color images automatically into multiple regions accurately, based on the local maxima of the GLCM texture property, with pixels spatially clustered into identical regions. A novel Clustering-based Image Segmentation using Local Maxima (CBIS-LM) method is presented. Our proposed approach generates reliable, accurate and non-overlapping multiple regions for the given user input image. The segmented regions can be automatically annotated with distinct labels which, in turn, help retrieve relevant images based on image semantics.

Color Image Segmentation Based on Secondary Watershed and GrowCut Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 4032-4037
Author(s):  
Jian Mei Chen ◽  
Hai Ying Lu
Keyword(s):  
Color Image ◽  
Automatic Segmentation ◽  
User Interaction ◽  
Input Image ◽  
Color Image Segmentation ◽  
Superior Performance ◽  
Segmentation Method ◽  
Interactive Algorithm ◽  
Segmentation Accuracy ◽  
Label Algorithm

GrowCut algorithm is not only an interactive algorithm on the basis of cell automata, but also a multi-label algorithm based on seeds point. Aiming at the GrowCut algorithm usually asks users to partition foreground and background manually and mark a lot more initial seeds. This paper presents an automatic object segmentation method which combining secondary watershed and GrowCut algorithm, here in the following paper refers it to as SWGC algorithm. It firstly using the twice used watershed algorithm to partition the input image, the segmented regions are labeled using Mahalanobis distance, and merged according to the image color and space information, thereafter applying the GrowCut algorithm to perform globally optimized segmentation. The main contribution focuses on performing automatic segmentation which consist of obtain the foreground and background region and generate the seed template of GrowCut algorithm automatically. Thus not only leave out the constraints of user interaction operation, but also avoid the subjectivity and uncertainty. The proposed method reduces the runtime significantly as well as improves the segmentation accuracy and robustness of GrowCut algorithm. Experimental results show SWGC algorithm has superior performance compared to the other related methods.

scholarly journals A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances

2021 ◽  
Vol 7 (10) ◽  
pp. 208
Author(s):  
Giacomo Aletti ◽  
Alessandro Benfenati ◽  
Giovanni Naldi
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Random Walk ◽  
Dirichlet Problem ◽  
Color Image ◽  
Color Image Segmentation ◽  
Computational Time ◽  
Random Walker ◽  
Segmentation Process ◽  
Colour Distance

Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of the image analysis. Colour based segmentation could hence offer more significant extraction of information as compared to intensity or texture based segmentation. In this work, we propose a new local or global method for multi-label segmentation that combines a random walk based model with a direct label assignment computed using a suitable colour distance. Our approach is a semi-automatic image segmentation technique, since it requires user interaction for the initialisation of the segmentation process. The random walk part involves a combinatorial Dirichlet problem for a weighted graph, where the nodes are the pixel of the image, and the positive weights are related to the distances between pixels: in this work we propose a novel colour distance for computing such weights. In the random walker model we assign to each pixel of the image a probability quantifying the likelihood that the node belongs to some subregion. The computation of the colour distance is pursued by employing the coordinates in a colour space (e.g., RGB, XYZ, YCbCr) of a pixel and of the ones in its neighbourhood (e.g., in a 8–neighbourhood). The segmentation process is, therefore, reduced to an optimisation problem coupling the probabilities from the random walker approach, and the similarity with respect the labelled pixels. A further investigation involves an adaptive preprocess strategy using a regression tree for learning suitable weights to be used in the computation of the colour distance. We discuss the properties of the new method also by comparing with standard random walk and k−means approaches. The experimental results carried on the White Blood Cell (WBC) dataset and GrabCut datasets show the remarkable performance of the proposed method in comparison with state-of-the-art methods, such as normalised random walk and normalised lazy random walk, with respect to segmentation quality and computational time. Moreover, it reveals to be very robust with respect to the presence of noise and to the choice of the colourspace.

scholarly journals Optimized Graph cut Color Image Segmentation using Genetic Algorithm with Weighted Constraints (OGcut)

2019 ◽  
Vol 8 (4S2) ◽  
pp. 583-587
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Color Image ◽  
Graph Cut ◽  
Input Image ◽  
Color Image Segmentation ◽  
Weighted Constraints ◽  
Mutation Operators ◽  
Cut Method ◽  
Crossover And Mutation

This paper proposes a Novel color image segmentation using Graph cut method by minimizing the weighted energy function. This method is applying a pair of optimal constraints namely: color constraint and gradient constraint. In the state-of-the-art methods, the background and foreground details are manually initialized and used for verifying the smoothness of the region. But in this proposed method, they are dynamically calculated from the input image. This feature of the proposed method can be used in color image segmentation where more number of unique segments exists in a single image. The genetic algorithm is applied to the graph obtained from the graph cut method. The crossover and mutation operators are applied on various subgraphs to populate the different segments.

True Color Image Segmentation by MUSIG Activation Function Using Self-Supervised QMLSONN Architecture With Context-Sensitive Thresholding

2018 ◽  
pp. 213-261
Author(s):  
Pankaj Pal ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Real Life ◽  
Activation Function ◽  
Input Image ◽  
Color Image Segmentation ◽  
Context Sensitive ◽  
True Color Image ◽  
Fuzziness Measure ◽  
True Color

In this chapter, the authors propose the true color image segmentation in real-life images as well as synthetic images by means of thresholded MUSIG function, which is learnt by quantum-formulated self-supervised neural network according to change of phase. In the initial phase, the true color image is segregated in the source module to fragment three different components—red, green, and blue colors—for three parallel layers of QMLSONN architecture. This information is fused in the sink module of QPSONN to get the preferred output. Each pixel of the input image is converted to the corresponding qubit neurons according to the phase manner. The interconnection weights between the layers are represented by qubit rotation gates. The quantum measurement at the output layer destroys the quantum states and gets the output for the processed information by means of quantum backpropagation algorithm using fuzziness measure.

scholarly journals Research on color image segmentation

2018 ◽  
Vol 25 (03) ◽  
pp. 138-143
Author(s):  
Wang He Xi Ge Tu ◽  
Bolormaa D
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Digital Image Processing ◽  
Color Image ◽  
Color Image Segmentation ◽  
Primary Analysis ◽  
Programming Tools ◽  
Segmentation Methods ◽  
Segmentation Image

The basic foundation for the development of the image processing is image segments. Primary analysis, such as analysis of images and visualization of images, begins with segmentation. Image segmentation is one of the important parts of digital image processing. Depending on the accuracy and accuracy of the segmentation, the results of the image analysis, including the size of the object, the size of the object, and so on. In the first section of this study, briefly describe the types of image segments. Also use Mathlab language's powerful modern programming tools to explore the image segmentation methods and compare the results. As a result of the experiment, it is more accurate to accurately measure the trajectory of the image segmentation of the image as a result of the Otsu-based method of B space. This will apply to further research. Өнгөний мэдээлэлд суурилсан дүрс сегментчлэх аргын судалгаа Хураангуй: Дүрс боловсруулах судалгааны ажлын үндсэн суурь нь дүрс сегментчлэл юм. Дүрсэнд анализ хийх, дүрсийг ойлгох зэрэг анхан шатны боловсруулалт нь дүрс сегментчлэхээс эхэлдэг. Дүрс сегментчлэл нь дижитал дүрс боловсруулалтын чухал хэсгүүдийн нэг юм. Сегментчлэлийг хэр зэрэг үнэн зөв, нарийвчлал сайтай хийснээс шалтгаалан, дараагийн дүрс таних, обьектын хэмжээ зэрэг дүрс шинжлэлийн алхамын үр дүн ихээхэн хамаардаг. Энэхүү судалгааны ажлын эхний хэсэгт дүрс сегментчлэх арга төрлүүдийн талаар товч танилцуулна. Мөн орчин үеийн програмчлалын хүчтэй хэрэгсэл болох Mathlab хэлний функцуудыг ашиглан дүрс сегментчилж гарсан үр дүнгийн харьцуулалтыг танилцууллаа. Туршилтын үр дүнд RGB өнгөний орон зайн B бүрэлдэхүүнд суурилсан Otsu-ийн аргийг ашиглан дүрсийг сементчилэх нь уламжлалт дүрс сегментчилэх аргаас нэн сайн үр дүнтай илүү нарийвчлалтай байна. Үүнийг цаашдын судалгааны ажилдаа хэрэглэх болно. Түлхүүр үг: RGB дүрс, босго (Threshold) утга, гистограм, Otsu-ийн арга, дүрс боловсруулалт

Roughness Measure Approach to Color Image Segmentation

2010 ◽  
Vol 36 (6) ◽  
pp. 807-816 ◽  
Author(s):  
Xiao-Dong YUE ◽  
Duo-Qian MIAO ◽  
Cai-Ming ZHONG
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation
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