Image segmentation based on color
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The document discusses a proposed algorithm for image segmentation that focuses on color-based methods for extracting color information from RGB images. The algorithm converts RGB images to HSV color space to better align with human perception and calculates the total number of distinct colors in an image, which has applications in areas like medical diagnostics. The paper outlines the steps of the algorithm and presents its effectiveness through results obtained from various images.
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 495
IMAGE SEGMENTATION BASED ON COLOR
Navkirat Kaur1
, V. K. Banga2
, Avneet Kaur3
1, 2, 3
Amritsar College of Engineering & Technology, Amritsar, India
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
-----------------------------------------------------------------------***----------------------------------------------------------------------
1. INTRODUCTION
The problem of image segmentation has been known and
addressed for the last 30 years. The general description of an
image in everybody mind is the list of objects in an image and
their positions .But when we deeply examine an image it
depicts shadows of an object, differences in the color
brightness of an object.[1] Image segmentation is the process
of partitioning an image into regions that are in some sense
homogeneous, but different from neighboring regions.[2].
Segmentation is the first key step in object recognition, scene
understanding and image understanding. Segmentation of an
image can be done on the basis of some characteristics such as
color, objects that are present in the entire image.[3]. The
level to which the separation is carried depends on the
problem being solved. [4].The result of image segmentation is
a group of different segments that mutually cover the entire
image.
Image segmentation algorithms are based on one of the two
basic properties of the intensity value i.e discontinuity and
similarity. There are different approaches for different type of
images. The first approach represents Histogram thresholding,
second approach is Edge based and the last one is region
based approach. In histogram thresholding different gray or
color ranges are represented to made regions of an image . In
the second approach, different edge detection operators are
used and also the edges are joined if the regions are not
connected. In the third approach images are partitioned into
regions which are similar according to a set of predefined
criteria [5, 6].
By concerning the image segmentation as the conundrum of
partitioning pixels into different clusters based on color
similarity and spacial relation, we propose our color image
segmentation method.[7]. It is a segmentation of an image
based on colors in it. According to this method, RGB image is
firstly converted to hsv image . HSV color space is used
because it gives the color according to human perception.
After that the image is converted to 3 rgb planes then the
pixels belonging to same color are merged together .After that
total numbers of colors are displayed.
This paper is organized in four sections. Section I give an
introduction. Steps for proposed algorithm are discussed in
section II. Section III discusses the results of the proposed
algorithm. Section IV gives Conclusion.
2. FLOW CHART FOR PROPOSED ALGORITHM
As the image segmentation is an emerging field in digital
image processing in some cases it becomes necessary to find
the total number of colors from the original image. It is very
useful in many medical applications some of which are
detecting brain tumors and cancerous cells. In the proposed
method each color present in an image is shown separately.
The procedure of calculating total number of colors from an
image is very helpful in quantization of image.
In the proposed work the original image which is in RGB is
converted to HSV color space .HSV color space is used
because it gives color according to human perception After
this process image is then converted to 3 RGB planes then the
pixels which have same color are merged together. At last the
total numbers of colors in an image are calculated.](https://image.slidesharecdn.com/imagesegmentationbasedoncolor-140807004615-phpapp01/85/Image-segmentation-based-on-color-1-320.jpg)
![IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
__________________________________________________________________________________________
Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 497
(2e) (2f)
(2g) (2h)
(2i) (2j)
(2k) (2l)
(2m)
Fig (2a) - (2m) depicts the extraction of each color from an
original image.
CONCLUSIONS
The proposed algorithm follows the image segmentation based
on colors at each pixel. This technique is capable because we
can get total number of colors from an image automatically
which is amazing. If we know the total number of colors then
it can be very helpful in medical applications.
REFERENCES
[1] Gudrun J. Klinker, Steven A. Shafer and Takeo Kanade,
“A Physical Approach to Color Image Understanding,”
International Journal of Computer Vision, Vol. 4, No. 1, pp. 7-
38, 1990
[2] Anil Z Chitade “Color based image segmentation using k-
means clustering” International Journal of Engineering science
and Technology, Vol. 2(10), 2010, 5319-5325
[3] R. C. Gonzalez and R. E. Woods, Digital Image
Processing. Addison-Wesley, 1993
[4] YZang“A review on image segmentation techniques with
remote sensing perspective” IAPRS Vienna, Austria, July 5–7,
2010, IAPRS, Vol. XXXVIII
[5] Navneet Kaur et.al “ Bacteria foraging based image
segmentation “An International Journal of Engineering
Sciences ISSN: 2229-6913 Issue July 2012, Vol. 6
[6] Rajeshwar Dass et.al “Image Segmentation Techniques”
IJECT Vol. 3, Issue 1, Jan. - March 2012
[7] Y. Zhong, K. Karu et.al, "Locating Text in Complex Color
Images," Pattern Recognition, vol. 28, no. 10, pp. 1523-1535,
October 1995](https://image.slidesharecdn.com/imagesegmentationbasedoncolor-140807004615-phpapp01/85/Image-segmentation-based-on-color-3-320.jpg)
Image segmentation based on color
- 1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 495 IMAGE SEGMENTATION BASED ON COLOR Navkirat Kaur1 , V. K. Banga2 , Avneet Kaur3 1, 2, 3 Amritsar College of Engineering & Technology, Amritsar, India 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 -----------------------------------------------------------------------***---------------------------------------------------------------------- 1. INTRODUCTION The problem of image segmentation has been known and addressed for the last 30 years. The general description of an image in everybody mind is the list of objects in an image and their positions .But when we deeply examine an image it depicts shadows of an object, differences in the color brightness of an object.[1] Image segmentation is the process of partitioning an image into regions that are in some sense homogeneous, but different from neighboring regions.[2]. Segmentation is the first key step in object recognition, scene understanding and image understanding. Segmentation of an image can be done on the basis of some characteristics such as color, objects that are present in the entire image.[3]. The level to which the separation is carried depends on the problem being solved. [4].The result of image segmentation is a group of different segments that mutually cover the entire image. Image segmentation algorithms are based on one of the two basic properties of the intensity value i.e discontinuity and similarity. There are different approaches for different type of images. The first approach represents Histogram thresholding, second approach is Edge based and the last one is region based approach. In histogram thresholding different gray or color ranges are represented to made regions of an image . In the second approach, different edge detection operators are used and also the edges are joined if the regions are not connected. In the third approach images are partitioned into regions which are similar according to a set of predefined criteria [5, 6]. By concerning the image segmentation as the conundrum of partitioning pixels into different clusters based on color similarity and spacial relation, we propose our color image segmentation method.[7]. It is a segmentation of an image based on colors in it. According to this method, RGB image is firstly converted to hsv image . HSV color space is used because it gives the color according to human perception. After that the image is converted to 3 rgb planes then the pixels belonging to same color are merged together .After that total numbers of colors are displayed. This paper is organized in four sections. Section I give an introduction. Steps for proposed algorithm are discussed in section II. Section III discusses the results of the proposed algorithm. Section IV gives Conclusion. 2. FLOW CHART FOR PROPOSED ALGORITHM As the image segmentation is an emerging field in digital image processing in some cases it becomes necessary to find the total number of colors from the original image. It is very useful in many medical applications some of which are detecting brain tumors and cancerous cells. In the proposed method each color present in an image is shown separately. The procedure of calculating total number of colors from an image is very helpful in quantization of image. In the proposed work the original image which is in RGB is converted to HSV color space .HSV color space is used because it gives color according to human perception After this process image is then converted to 3 RGB planes then the pixels which have same color are merged together. At last the total numbers of colors in an image are calculated.
- 2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 496 yes no Fig 1: Flow chart for the proposed algorithm The step wise processes for the proposed algorithm are as follows: Step 1: The very first step is to read an RGB image Step 2: The next step is to convert RGB image to HSV. This color space is used because it gives the color according to human perception. Step3: Further three matrix are made by three different planes. Step4: A single new matrix is formed so as to see values of RGB at each pixel Step5: If two rows are equal in a single new matrix then combine those rows Step6: Total number of colors existing in an original image is calculated Step7: Enter the number of colors user wants to see Step8: Image is converted from HSV to RGB color space 3. RESULTS AND DISCUSSIONS We applied our algorithm on various RGB images and as a result we get total number of colors from the given RGB image. The below figure depicts the original RGB image and the other images which are shown below contains the extraction of colors from an original image. The main feature of this algorithm is that, it can calculate the colors in an image automatically which makes the process faster as compare to other algorithms. Fig 2: Original image (2a) (2b) (2c) (2d) Read color image Convert image from rgb to hsv Form rgb planes Extract rgb values of each pixel Form single new matrix with 3 RGB planes If more than 1 same row exists in a matrix Start Calculate total new no. of rows in matrix. This will represent m total no. of colours in input image Enter no. of colours to be displayed Select 1 st n rows from total n rows of new matrix Convert hsv to rgb Display the image End Extract extra rows and form a new matrix
- 3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 __________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org 497 (2e) (2f) (2g) (2h) (2i) (2j) (2k) (2l) (2m) Fig (2a) - (2m) depicts the extraction of each color from an original image. CONCLUSIONS The proposed algorithm follows the image segmentation based on colors at each pixel. This technique is capable because we can get total number of colors from an image automatically which is amazing. If we know the total number of colors then it can be very helpful in medical applications. REFERENCES [1] Gudrun J. Klinker, Steven A. Shafer and Takeo Kanade, “A Physical Approach to Color Image Understanding,” International Journal of Computer Vision, Vol. 4, No. 1, pp. 7- 38, 1990 [2] Anil Z Chitade “Color based image segmentation using k- means clustering” International Journal of Engineering science and Technology, Vol. 2(10), 2010, 5319-5325 [3] R. C. Gonzalez and R. E. Woods, Digital Image Processing. Addison-Wesley, 1993 [4] YZang“A review on image segmentation techniques with remote sensing perspective” IAPRS Vienna, Austria, July 5–7, 2010, IAPRS, Vol. XXXVIII [5] Navneet Kaur et.al “ Bacteria foraging based image segmentation “An International Journal of Engineering Sciences ISSN: 2229-6913 Issue July 2012, Vol. 6 [6] Rajeshwar Dass et.al “Image Segmentation Techniques” IJECT Vol. 3, Issue 1, Jan. - March 2012 [7] Y. Zhong, K. Karu et.al, "Locating Text in Complex Color Images," Pattern Recognition, vol. 28, no. 10, pp. 1523-1535, October 1995