Presentation on theme: "Segmentation in color space using clustering Student: Yijian Yang Advisor: Longin Jan Latecki."— Presentation transcript:
Segmentation in color space using clustering Student: Yijian Yang Advisor: Longin Jan Latecki
Index Introduction –Segmentation –Color Space Segmentation using both color space and spatial space information –Basic concept –DBScan algorithm Result
Segmentation Definition A process of separating of individual perception of the scene. Importance The first step in image processing.
Segmentation Techniques Histogram thresholding Assumption: The image is composed of regions with different color ranges. Edge-based approach Difficulty: Need for joining separated edges. Region-based approach –Region growing –Region splitting –Region merging –Their combination
Color Space There are many different existing color space Example: RGB, YUV, HSV, Munsell system and so on. Munsell color system is the best in simulating the human color vision. Munsell Hue, Munsell Value, Munsell Chroma
RGB to HVC
Basic Concept(1) Distance
Basic Concept (2) Eps of a point The neighborhood within a radius of a given point. Core point If the Eps of a point contains at least a minimum number, MinPts, of points with similar property, it is a core point.
Basic Concept(3) Directly density-reachable Point p is directly density-reachable from a point q if It is obviously, directly density-reachable is symmetric for pairs of core point. But in general, it is not symmetric if one core point and one border point are involved.
Basic Concept(4) Density-reachable A point p is density-reachable from a point q wrt. Eps and MinPts if there is a chain of points p 1, p 2, …, p n, p 1 =q p n =q such that p i+1 is directly density-reachable from p i.
Basic Concept(5) Density-connected A point p is density-connected to a point q wrt. Eps and MinPts if there is a point o such that both p and q are density-reachable from o wrt. Eps MinPts.
Density-based Algorithm 1.Search the unlabeled pixels in an image in order of current core pixel and current core region. 2.If a core pixel p is found, a new cluster is created. Then we iteratively collect unlabeled pixels that are density-connected with p, and label these pixels with same cluster label. 3.If there are still existing core pixels in the image, goto 2. 4.For the pixels that are not included in any clusters, merge them with the cluster that is adjacent to them and has the highest similarity in average color value with them 5.Label each cluster we find in the image as a segmentation region.
Spatial Eps and Minpts Spatial Eps Simply select the size of Spatial Eps circle according to the size of image. The larger the image is, the larger its Spatial Eps circle will be. MinPts MinPts is set to half of the number of pixels in the Spatial Eps circle, which means that we will consider the pixels whose color are same as the dominant color in the circle.
Color space Eps To determine the value of H radius, V radius, and C radius.
Program List Segmentation:Main functionSegmentation RGB2HVC:Transformation function from RGB to HVCRGB2HVC Get_radius:Calculate H, V, C radiusGet_radius ExpandCluster:Region growing methodExpandCluster Region Query:Finding homogeneous pixelsRegion Query Color_distance:Calculate HVC color space distanceColor_distance Spatial_distance:Calculate spatial space distanceSpatial_distance ChangeAllId:Change label functionChangeAllId Show_segmentation:Display the resulting image according to different label.Show_segmentation
Result 1 Original PictureSegmented Pictures
Result 2 Original PictureSegmented Pictures
Result 3 Original PictureSegmented Pictures
Result 4 Original PictureSegmented Pictures
Reference Color image segmentation using density-based clustering A density-based algorithm for discovering clusters in large spatial databases with noise Density-based clustering in spatial databases: the algorithm GDBSCAN and its application Color space analysis and color image segmentation Color segmentation techniques