ppt on region segmentation by AJAY KUMAR SINGH (NITK)
Ajay Kumar Singh
Classification of methods
Region based segmentation
Segmentation refers to the process of
partitioning a image into multiple regions.
Regions:- A group of connected pixels
with similar properties.
Regions are used to interpret images. A
region may correspond to a particular
object, or different parts of an object.
most cases, segmentation should
provide a set of regions having the
Connectivity and compactness
Regularity of boundaries
Homogeneity in terms of color or texture
Differentiation from neighbor regions
Need of segmentation
The goal of segmentation is to simplify the
representation of an image into something that is
more meaningful and easier to analyze.
Image segmentation is typically used to locate
objects and boundaries in images.
For correct interpretation, image must be
partitioned into regions that correspond to
objects or parts of an object.
Let R represent the entire image region. We
want to partition R into n sub regions, R1,
R2, . . ., Rn, such that:
(a) Summation of Ri =R
(b) Ri is a connected region for i=1, 2, . . , n
(c) Ri intersection Rj =φ for all i and j , I≠ j
(d) P(Ri) = TRUE for i=1, 2, . . . n
(e) P( Ri summation Rj)= False , i ≠ j
(a) segmentation must be complete
– all pixels must belong to a region
(b) pixels in a region must be connected
(c) Regions must be disjoint
(d) states that pixels in a region must all share the
– The logic predicate P(Ri) over a region must return
TRUE for each point in that region
(e) indicates that regions are different in the sense of
the predicate P.
Region based approaches
group together pixels with similar
combining proximity and similarity
Region based approaches are based on
pixel properties such as
The most used methods are
Split and merge
Pixel Aggregation (Region
The basic idea is to grow from a seed pixel
At a labeled pixel, check each of its neighbors
If its attributes are similar to those of the already labeled
pixel,label the neighbor accordingly
Repeat until there is no more pixel that can be labeled
For example, let
The attribute of a pixel is its pixel value
The similarity is defined as the difference between
adjacent pixel values
If the difference is smaller than a threshold, they are
assigned to the same region, otherwise not
Region Growing : Algorithm
a) Chose or determined a group of seed pixel
which can correctly represent the required
b) Fixed the formula which can contain the
adjacent pixels in the growth;
c) Made rules or conditions to stop the growth
Region Split and Merge
After segmentation the regions may need to be
refined or reformed.
Split operation adds missing boundaries by
splitting regions that contain part of different
Merge operation eliminates false boundaries and
spurious regions by merging adjacent regions that
belong to the same object.
Split-and-merge in a hierarchical data structure
Algorithm: Region Splitting
initial region in the image
For each region in an image,
Compute the variance in the gray values for
If the variance is above a threshold, split
the region along the appropriate boundary
If some property of a region is not constant
Regular decomposition Methods: divide the region
into a fixed number of equal-sized regions.
Algorithm: Region Merging
(1) Form initial regions in the image using thresholding ( or
a similar approach) followed by component labeling.
(2) Prepare a region adjacency graph (RAG) for the image.
(3) For each region in an image, perform the following
(a) Consider its adjacent region and test to see if they are similar.
(b) For regions that are similar, merge them and modify the RAG.
(4) Repeat step 3 until no regions are merged.
In image compression
MPEG-4 video object (VO) segmentation
 Rafael C. Gonzalez and Richard E. woods “DIGITAL
IMAGE PROCESSING,ˮ 2011.p. 762-770.
 Jun Tang, “A Color Image Segmentation algorithm
Based on Region Growing,ˮ China School of Electronic
 Chaobing Huang, Quan Liu, Xiaopeng Li “Color Image
Segmentation by Seeded Regionˮ China, School of
information engineering, 2010.
 Tiancan Mei, Chen Zheng, Sidong Zhong, “Hierarchical
Region Based Markov Random Field for Image