REGION-BASED IMAGE SEGMENTATION

ByAjay Kumar Singh

Overview

Definition Need of segmentation Classification of methods Region based segmentation

Definition

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.

In most cases, segmentation should provide a set of regions having the following properties 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.

Basic Formulation

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

Basic Formulation

(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

same property – 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.

Segmentation Effect

Region Segmented Image

Approaches to segmentation Region based approaches

group together pixels with similar properties

combining proximity and similarity

Classification

Region based approaches are based on pixel properties such as

Homogeneity Spatial proximity

The most used methods are Thresholding Clustering Region growing Split and merge

Pixel Aggregation (Region Growing) 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 region;

b) Fixed the formula which can contain the adjacent pixels in the growth;

c) Made rules or conditions to stop the growth process

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 objects. 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 Form initial region in the image For each region in an image,

recursively perform: Compute the variance in the gray values for

the region 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

steps: (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.

Applications

In image compression Object recognition Computer graphics Medical Imaging MPEG-4 video object (VO) segmentation

References[1] Rafael C. Gonzalez and Richard E. woods “DIGITAL

IMAGE PROCESSING,ˮ 2011.p. 762-770.[2] Jun Tang, “A Color Image Segmentation algorithm

Based on Region Growing,ˮ China School of Electronic Engineering 2010.

[3] Chaobing Huang, Quan Liu, Xiaopeng Li “Color Image Segmentation by Seeded Regionˮ China, School of information engineering, 2010.

[4] Tiancan Mei, Chen Zheng, Sidong Zhong, “Hierarchical Region Based Markov Random Field for Image Segmentation”,Wuhan,China,2011

[5] en.wikipedia.org/wiki/Region_growing

Thanks to All

Any Question??