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IMAGE SEGMENTATION OF HIGH RESOLUTION IMAGES

Presented By, Prashant Mishra

Jeet Patalia

Introduction

• Image Segmentation:

• Process of partitioning a digital image into multiple segments.

• To segregate image for a more meaningful and simpler analysis

• Grouping pixels with similar characteristics of color, intensity & texture.

Introduction (contd…)

• Various Image Segmentation techniques…

1. Thresholding

2. Region Based

3. Clustering

4. Graph Partitioning

5. Watershed Based

• Image Resolution : It is the details an image holds.

• Term applies to raster digital images.

• Resolution quantifies how close lines can be to each other and still be visibly

resolved

(Contd…)

• Resolution can be described as Pixel, Spatial, Spectral, Temporal &

Radiometric resolution.

• High resolution images consists of greater pixel density

• High resolution images has rich structural or spatial information of image

objects.

• Thus Image segmentation of High resolution images provides an important

analysis of the details provided in the images.

WATERSHED TECHNIQUE

• Region based Image segmentation approach.

• Watershed method is a powerful mathematical morphological tool .

• Watershed means the ridge that divides areas drained by different river systems.

• If image is viewed as geological landscape, the watershed lines determines

boundaries which separates image regions.

• The watershed transform computes catchment basins and ridgelines (also known

as watershed lines), where catchment basins corresponding to image regions and

ridgelines relating to region boundaries.

Topographic View

• A Grayscale image:

• Any grey tone image can be considered as a topographic surface :

• Gray level image as a topographic relief

• Gray level interpreted as altitude of the relief

Watershed Concept

• Basic concept of watershed algorithm is to find the watershed lines.

• We flood this surface from its minima 

• And we prevent the merging of the waters coming from different sources.

• We partition the image into two different sets: the catchment basins and the

watershed lines. 

Example

• Gray image: - Gray to topographic by the means of edge detector

• Topographic View: -Height corresponds gray value - Local minima has been found , indicated by red dots(multiple local minimum due to residual noise )

Ex. Contd…• Flooding starts from the minima

• High ridges prevents merging of 2 different catchment basins

Ex. Contd…

• Flooding reaches a point , where only top of high ridges visible.

• Wherever there was ridges , watershed has been erected

- Watershed is a natural divide between 2 local minima.

Drawbacks & Solution

• In practice, this transform produces an important over-segmentation due to

noise or local irregularities in the gradient image. 

• A major enhancement of the watershed transformation consists in flooding

the topographic surface from a previously defined set of markers. 

• Doing so, we prevent any over-segmentation.

Example

Graph Partitioning

Vertices and EdgesA graph G = (V, E), is constructed with collection of vertices, V and edges, E. This graph theory is generally used in modelling problems such as traffic networks, electrical circuits and internet networks. To form graph in image context, the vertices are represented by the smallest element in the image, namely pixels.The edges, E, are set of elementsconsisting similarities and dissimilarities between pixels.

Weight

• Prior to segmentation, initialising a weighted graph is required to construct

the connectivity information of the pixels in an image.

• The edge set of E has each of them assigned with a weight, w(i, j). Each w(i,

j) is a measurement of similarity between pixel i and pixel j.

• The value of w(i, j) increases with the similarity degree between pixel i and

pixel j.

Cuts

• Graph partitioning is done by cutting out edges with low value of weight.

• Weak weight of paired pixels indicates low similarity between the paired

pixels. Hence, to partition the graph into two sub-graphs, the minimum cuts

across edges are determined by finding the minimum value of,

cut (A ,B)= Σ w (I, j)

• whereby A and B are sub-graphs with constraint of A ∪ B = V, A ≠ Ø, B ≠

Ø, and A∩B = Ø .

• A minimum cut criterion is introduced by Wu and Leahy such that it

minimised the possible maximum cuts available across the sub-graphs.

Graph partitioning on an image with (a) the original image (b) thegraph model, dashed lines denote weak similarity and non-dashed lines

denotes strong similarities.

NORMALISED CUTS IN IMAGE SEGMENTATION

• Image segmentation with minimum cuts does give correct segmentation result but it favours in cutting out isolated pixels.

• Shi and Malik proposed an improved cuts algorithm named normalised cuts to alleviate the isolated pixels problem by introducing a disassociation measure for

• normalised cut,• Ncut as shown in (2),

Ncut (A, B)= cut (A, B) + cut (A, B)

assoc (A, V) assoc (B, V)

• whereby assoc(A, V) = Σi∈A,j∈V w(i, j) and assoc(B, V) = Σ i∈B,j∈V w(i,j).

• The proposed measure eliminates the occurrence of partition that cuts out isolated small set of pixels by formulating the cut as a fraction of the total edges that paired with all the pixels in the graph.

• The relation between disassociation and association can be described as in

• Based on Eq. 3, minimising the dissociation between partitions denotes maximising the association between partitions

Multi fractal Image Segmentation

A multifractal system is a generalization of a fractal system in which a single

exponent is not enough to describe its dynamics; instead, a continuous

spectrum of exponents (the so-called singularity spectrum) is needed.

The edge content of very high resolution images, such as those from Ikonos,

is very important due to the huge amount of details provided.

Classical methods usually fail to achieve a good segmentation result on such

images.

Hence, we used a method for high resolution optical image segmentation

which is based on the multifractal characterization of the image.

In remote sensing, the recent availability of really high spatial resolution

images brings the texture based segmentation issue to a higher level of

complexity.

The textures are difficult to charecterise due to very high local variability of

pixel values.

One of the main advantage of the multifractal analysis is that one does not

need any a priori information about the signal.

Now multi fractal segmentation is done as first of all starting from the

analysis of the Holder regularity at each point, we extract features leading to

the segmentation of the image. Based on information from the high

frequencies, we use a k-means clustering algorithm to perform the

segmentation.

Let α be a positive real number and x0 R; a function f: R → R is Cα(x0) if ∈

it exists a polynomial function P of maximum degree [α] such that:

|f(x) − P(x − x0)| ≤ C|x − x0|^α

where [α] = α − 1 if α is an integer.

The original image made of 5 brodatz textures

(a) The multifractal spectrum (b) Co-occurrence method based algorithm

Ikonos image (a) The multifractal spectrum based (b) Co-occurrence method algorithm

The co-occurrence technique gives 67% of good classification and the

multifractal spectrum based algorithm 81%.The second test was done on an

635×563 Ikonos panchromatic image of a forestry sceneWe can clearly

distinguish different tree density classes, as well as two lakes and unstocked

areas.

The multifractal spectrum based algorithm gives more compact segments. On

the other hand, the co-occurrence analysis cannot detect the lakes and the

classes are not well defined.

Conclusion

We can conclude that the multifractal analysis is an interesting tool for the

texture analysis because it takes into account local and global information

about the singularity distribution of the signal.

Thus multifractal algorithm gives rather good results then the other method(s).

Reference • Segmentation of High-resolution Remote Sensing Image Based

on Sun 1.2 Marker-based Watershed Algorithm Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing

• Image Segmentation via Normalised Cuts and Clustering Algorithm Mei Yeen Choong, Wei Yeang Kow, Yit Kwong Chin, Lorita Angeline, Kenneth Tze Kin Teo Modelling, Simulation & Computing Laboratory, Material & Mineral Research Unit