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International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 4 Issue 2 February 2015, Page No. 10310-10312
A Comprenssive Survey On Different Methods For Detecting
Salient Object
Manish M. Paliwal1, Dr. Prashant N. Chatur2
1
PG Scholar, Government College of Engineering,
Department of Computer Science and Engg., Amravati. INDIA
paliwalmanish1@gmail.com
2
HOD, Government College of Engineering,
Department of Computer Science and Engg., Amravati. INDIA
chatur.prashant@email.com
Abstract: This survey paper consists of various image processing techniques used for the detection of the salient region. The paper also
includes the comparative study of these techniques like precession recall analysis and the relative advantages and disadvantages. The major
topic of our interest is the Salient Region of the object which is an important factor in most of the areas like cognitive psychology and
neurobiology. The various technique are different in there logic and performance the involved. Comparing to all the techniques on there
both positive and negative sides the paper presented here is the best in my behalf to its domain knowledge.
Keywords: GMM , Spatial Distribution , Rarity , Visual Uniqueness etc.
1. Introduction
In image processing domain a large number of problem
domains are available. One of area of our interest is Detection
of Salient Region in the given image. In the image processing
operation the saliency can be considered as the root problem
because it affects the large number of the natural problems.
When we observe some scenes then we noticed that during
taking the picture the human focused on the some particular
object mostly compared to the other objects in similar image.
The particular object is called as Salient object where the
human put its most of the attention. Sometime it is also called
the object of interest. The researchers devoted a lot of time on
various techniques in this area. But still no method can achieve
the full optimum performance what we required. Some methods
performs well on their execution time but compromises with
the available space. Some methods restricted in the shape and
size of the image. A detailed survey described below in this
paper.
2. Different Methods
A. Soft Image Abstraction Technique
The Soft Image abstraction technique is one of the best
available techniques to detect the salient object inside the
image. The main idea behind this method is to decompose the
given image into various small regions which bear some spatial
relationship with respect to each other. Once we have
decomposed the image the next phase is to assign the global
Saliency values to the image pixels. The assignment of the
image saliency values is done on the basis of the weighted
color contrast with respect to the all other components. For the
sake of simplicity and better precision we took the concept of
the Gaussian Mixer Model. GMM define the probability of the
pixel belonging to the one cluster. The clustering achieved
through the Orchard and Bauman’s Algorithms which initially
starts with one cluster and iteratively uses the eigenvalues and
eigenvector of the covariance matrix to specify the cluster
splitting point. The Clustering divide the color channel into 12
different parts and choose only those color which comprises
approx. 95% of the image pixels.
In Soft image abstraction method the probability of the image
color Ix belonging to the component c is given by the following
formula(1)
Where Wc,µc and ∑c indicate the weight, mean color and the
covariance matrix of the cth component.
The method divide the color channel into 12 different band so
that number of color should be minimum from computational
point of view. Also it takes only those image color which
account a minimum of 95% of the whole image. The idea
comes from the fact that not all color contribute to the image
formation so take only relevant colors.
Now the spatial distribution among the two GMM components
can be found by the correlational formula given as follow(2)
After computing the correlational matrix it is necessary to
perform the clustering operation on the available component.
Such clustering can be achieved by the message passing
clustering because it does not require the predefined cluster
numbers. When we perform the clustering then the probability
of each pixel color Ix belonging to the cluster C becomes-
Manish M. Paliwal1 IJECS Volume 4 Issue 2 February, 2015 Page No.10310-10312
(3)
Page 10310
Where Ib is the quantized histogram bin color of Ix and C
denotes the cluster.
We know that high contrast is a good indication of the saliency
so the visual uniqueness of the image component can be
computed by the following formula-
(4)
Where D(ci,cj) is the spatial distance between centroid of the
two GMM component ci and cj and the value of σ2 is taken as
0.4.
The experimental result shows that the soft image abstraction
method capable to reduce the mean absolute error by 25%. The
method is also better in terms of the execution time.
B. Salient Region Detection using Saliency Filters
This method is proposed by the Federico Perazzi and et al. The
concept of their method is somewhat similar to the previous
methods but it was new as it makes use of the Gaussian Filter
for the Contrast and Saliency estimation. The methods consist
mainly four basics steps. In first step it decomposes the given
image into compact, perceptually homogeneous element and
abstract out the unnecessary details which are irrelevant for the
further analysis. Based on this they check for the two important
features in the image element which are pixel contrast and
spatial distribution of the image pixel. The pixel contrast value
determines the rate of uniqueness of each pixel.
During the first phase of the method the main task is to keep
the decomposed element as small as they could preserve the
relevant structure and and the same time abstract out
unnecessary details.. This type of decomposition is mainly
achieved by the edge preserving localized over segmentation
based on color. Second phase of the method is to compute the
two measure of the contrast. For that they compare each of the
image pixels from the other pixel to find the feature called
“rarity”. As we commonly know that the image pixel of the
background are highly distributed on the entire image compare
to the foreground image pixels but it doesn’t meant that they
can be consider the salient. So it is necessary that some large
scale image segmentation techniques should be adopted to get
this property. The rarity of the image pixel is computed by the
following way.
(5)
Where
Ui = uniqueness of the pixel segment i
Pi= Position of the ith pixel segment
Ci= color of the segment i in CIELab
The complexity of the above operation is O (N2) where N is the
number of image segment. But making use of the Gaussian
weight wij it is possible to reduce the complexity up to O(N).
For element Distribution measure of a segment i they computed
spatial variance Di of its color ci. Low variance indicates the
compact object which is more salient than the spatially widely
distributed element. The Di can be computed as follow-
(6)
Where
µi = weighted mean position of the color ci.
Again the equation of the spatial distribution has the quadratic
complexity but by introducing the Gaussian function we can get
the linear time complexity.
Saliency assignment is the next step towards the combining
both feature uniqueness and spatial distribution. They
normalized the value of both factor in the range of (0…1).
These two values are independent so we can compute the
Saliency value Si for each element
(7)
For there experimental purpose they fixed the value of k as 6
which is a scaling factor for the exponential function. The final
saliency value for image pixel Si is defined by the weighted
linear combination of the saliency Sj of its surrounding image
elements.
(8)
The method is good in overall performance tested over the best
processing architecture.
C. Salient Object Detection using Regional Contest Based
algorithms
The method was suggested by the Ming Mang Cheng et al. This
method was based on the Histogram based contrast method.
The idea was similar to the other salient object detection
method but in differ in terms of saliency value calculation.
The saliency of a pixel in the image is defined by the color
contrast value of the all other pixel in the same image. The
saliency value function for the calculation can be given as
follow(9)
Where
D(Ik,Ii) is the color distance matrix between the pixels Ik and Ii
in the image.
Pixel with the same color has same saliency value by this
equation. As the number of the image color are very large so
the complexity of calculation is near O (N2) but it can be
brought down to linear by reducing the number of image color.
Instead of taking 256 different colors we quantized the color in
the 12 different color bands. So it reduces the total available
color. Again to get the better result we took only those colors
which comprise the 95% of the overall image.
The methods relay on the fact that the high contrast to ones
surrounding region is stronger for the saliency of the region.
For that purpose they introduce the region contrast (RC)
method. They first segment the image into regions and then
make a color histogram for the each region. The saliency value
of each region is calculated as follow(10)
Where w(ri) is the weight of region ri and Dr(.,.) is the color
distance matrix between two region.
For the space efficiency consideration we take the sparse
histogram representation as the number of color in image
region is very small. They also observe that the image region
with a large boundaries overlapped by the other region are
often treated as non-salient object.
The performance of this method is compared against the
available 15 different methods and found suitable for the
detection. The HC method possesses the linear complexity. The
variation of the HC is the RC method which is somewhat
complex but yields a superior quality saliency maps.
Manish M. Paliwal1 IJECS Volume 4 Issue 2 February, 2015 Page No.10310-10312
Page 10311
C. Supervised approach for salient object detection using
condition random field
This method was proposed by the Tie Liu et al. The idea of
their method comprises of two steps. First they model the
salient object detection problem by a condition random field
(CRF) where they combine the multiple features through the
CRF learning. They also propose a set of novel local, global
salient feature to specify generic salient object.
They initially provide the solution for the single image then
they extended their idea for the sequential images. They define
the problem as binary labeling task. The probability of a
labeling configuration A={ax} is given by following
conditional random function(11)
Where Z is the partition function
They defined the energy E(A|I) as follow-
(12)
Where
λk is the weight of kth feature and x, x’ are two adjacent pixels
Fk(ax,I) indicate probability of a pixel x belongs to the salient
object.
M. M. Paliwal received his B.E. degree in
Computer Science and Engineering from
University institute of Technology,RGPV,
Bhopal, Madhya Pradesh, India in 2012,
pursuing M.Tech degree in Computer Science
and Engineering from Government College of
Engineering, Amravati, Maharashtra, India.
His research interest includes image
processing, Algorithm Analysis, Operating System concept and Data
mining. At present he is engaged in Salient
Region detection Technique which will be
giving a better performance than the existing
Region Detection Techniques.
Dr. P. N. Chatur has received his M.E
degree in Electronics Engineering from
Government
College
of
Engineering
Amravati, Maharashtra, India and Ph.D.
degree from Amravari University. He has published twenty papers in
international journals. His area of research includes Artificial Neural
Network, Data Mining, Data Stream Mining and Cloud Computing.
Currently, he is Head of Computer Science and Engineering &
Electronics Engineering Department at Government College of
Engineering Amravati, Maharashtra, India. At present he is engaged
with large database mining analysis and stream mining.
3. Conclusion
In this survey paper the aim is to study the different techniques
available for the salient object detection on their both positive
and negative sides. Some of the techniques are best in their
complexity related issue but didn’t applicable on the all image
size as they are restricted in the image window size. Some of
the methods possess faster execution time on the all available
system architecture. Beside this our aim is to direct the research
of salient object detection in specific direction so that the
difficulties which are still present can be solved. We hope that
our review will direct the researcher to work well in this
direction to achieve better efficient solution.
References
[1] Ming Ming Cheng and Jonathan Warrell , “Efficient
Salient Region Detection with Soft Image Abstraction”
IEEE International Conference on Comuter Vision,2013.
[2] F. Perazzi, P. Kr¨ahenb¨uhl, Y. Pritch, and A. Hornung,
“Saliency filters: Contrast based
filtering for salient
region detection”, In IEEE CVPR, pages 733–740, 2012.
[3] Tie Liu, Zejian Yuan, “Learning to Detect a Salient
Object,” IEEE Transactions on pattern Analysis and
Machine Intelligence. vol. 33, February 2011.
[4] Ming Mang Cheng, Guo-Xin Zhang, “Global Contrast
based Salient Region Detection,” In IEEE CVPR,pages
409-416,2011.
[5] Niloy J. Mitra, Xiaolei Huang, “Salient Object Detection
and Segmentation,” IEEE Trans. On Pattern, Technical
Report, TPAMI-2011-10-0753, 2011.
Author Profile
Manish M. Paliwal1 IJECS Volume 4 Issue 2 February, 2015 Page No.10310-10312
Page 10312