 Open Access
 Total Downloads : 479
 Authors : Miss. Hiteshri S.Khandre, Prof. P.S.Kulkarni
 Paper ID : IJERTV2IS90551
 Volume & Issue : Volume 02, Issue 09 (September 2013)
 Published (First Online): 21092013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
A Recursive Ant Colony System for Feature Extraction & Edge Detection
Miss. Hiteshri S.Khandre

ech IV Sem,Computer Science & Engg.
RTM Nagpur University ,
Rajiv Gandhi College Of Engg.,Research & Technology,Babupeth,Chandrapur, India.(+91)
Abstract
Edge detection is one of the important techniques in image processing which is used in many applications. It generally detects the contour of an image and thus provides important details about an image which cam further used in tasks like object recognition and image segmentation. The most important step in the edge detection, on which the success of generation of true edge map depends, lies on the determination of threshold. This paper gives nature inspired solution like ant colony optimization (ACO) in edge detection and feature extraction. Recursive ant colony optimization technique is proposed here to find out the solution. The success of the proposed work is tested visually with the help of test images and empirically tested on the basis of several statistical parameter of comparison. Wavelet thresholding method is used to check the performance of feature extraction method.
Keywords
Recursive Ant Colony System, Edge detection, Partitioning,2opt,ACS, Heuristic Search

INTRODUCTION
Image processing and machine vision have for long been a vital element in various fields of technology. While automated and artificially intelligent systems in many cases require a significant amount of precise and robust image processing capabilities, robust techniques are yet to be proposed and demonstrated for many ongoing problems. Image edge detection refers to the extraction of the edges in a digital image. It is a process whose aim is to identify points in an image where discontinuities or sharp changes in intensity occur. This process is crucial to understand the content of an image and has its applications in image analysis and machine vision. As a meta heuristic algorithm, ant colony optimization (ACO) has the features of robust, parallel, positive feedback, which prove it to be a useful means for searching optimal results from the problem. ACO has been used to solve the problem like Edge detection & Feature
Prof. P.S.Kulkarni
Department Of Information Technology, Rajiv Gandhi College Of Engg.,Research & Technology, Babupeth, Chandrapur, India(+91)
extraction. Edge is one of the simplest and the most important features of image, and this feature is broadly used in image recognition, segmentation, enhancement and compression . The purpose of edge detection is not only to extract the edges of the interested objects from an image, but also to lay the foundation for image fusion, shape extraction, image segmentation, image matching, and image tracking. Ant colony optimization (ACO) is one of the most recent techniques for approximate optimization, which based on the foraging behavior of real ants. Ants communicate by means of a kind of substance called pheromone which can enable them to find the shortest paths (the most preferred ways) between their nest and food sources . Edge detection is an important content of image processing and lowlevel computer vision. Edges represent important contour features in image. Image edge is usually defined as the sudden change areas of gray value. The basic idea of the traditional gray gradientbased edge detection algorithms is the comparison of pixels gray value. Therefore, pixels gray gradient value can be used to be the feature of edge
Automatic detection of edges in image is a classical problem in computer vision and image processing. Image edge detection refers to the extraction of the edges in a digital image. It is a process whose aim is to identify points in an image where discontinuities or sharp changes in intensity occur. This process is crucial to understand the content of an image and has its applications in image analysis and machine vision. As a metaheuristic algorithm, ant colony optimization (ACO) has the features of robust, parallel, positive feedback, which prove it to be a useful means for searching optimal results from the problem. ACO has been used to solve many complex problems successfully such as the TSP, quadratic assignment problem, graph coloring, and data mining. Compared with other heuristic algorithms, ACO is a populationbased approach which uses exploitation of positive feedback as well as greedy search. Because of its
parallel and discrete features, ACO is more suitable for image processing problems, such as segmentation, feature extraction, image matching and texture classification. Here our aim is to design an algorithm for image edge extraction which can be tuned using different parameters for satisfying performance in the presence of noise.
w() is a weighting function, this function ensures that very sharp turns are much less likely than turns through smaller angles.
The feature of a pixel (i, j) is presented as below:
I
F a. ij b.E
The rest of this paper is organized as follows.
Section II presents basics of principle component
ij I
ij
max
analysis. Section III gives mechanism of PCA. Section IV review of existing algorithms. Section V describes the Ant Colony System in detail. Section VI presents Recursive Ant Colony System, a new approach based on Ant Colony System. Finally, in Section VII, we report the conclusion.

REVIEW OF EXISTING ALGORITHM

An Ant Colony Optimization Algorithm for Image Edge Detection[4] Gradient feature is simple but it is sensitive to noise and texture. Relative Difference of Statistical Means have strong ability to suppress noise but edge information may lose. Therefore authors have combined gray gradient value of pixel and relative difference of statistical means to image edge detection.
E 0, E1 E 2 0
n n n
2 E1 E 2
Where a and b are weighting factors.Imax denotes the maximum value of the gradient in image.
Decision Process
Finally, a binary decision is made at each pixel location to determine whether it is on the edge or not, by applying a threshold T on the final pheromone matrix.

Ant Colony Search for Edge Detection[5]
In this paper, a heuristic ant colony search algorithm is proposed to overcome the shortcoming of traditional edge extracting methods. Algorithm uses Sobel operator to get the possible edge points.
Heuristic measure is the key process used in this paper and given by following equation Heuristic information is related to the gradient and phase of the transition route.
f(i,j)>gradient of node (i,j)>phase of node i,j 1 , 2>constants where 1 + 2 =1
(i,j)(r,s) > directional difference of ant move from
no
n n , E1
de
E 2 0
1
1
E n
E 2 n n n
(r, s) to (i, j)
En is the relative difference of Statistical Means.
(r ,s) w( (r ,s) )[ f 1 ]
f (x, y)
Ek x, yD k 1,2; n 0 3
(i, j )
(i, j )
1 (i, j )
2
(i, j )
(r ,s)
n N
x, yD
n
n
Stopping criterion
Smax is the stop theshold
Stopping point is the end of the edge transited by the ant. In each step of transition, the ant estimates
4
Probability Decision
p
p
( ij ) (ij ) wij ()
ij ( ij ) (ij ) wij ( )
j
whether the stoppin criterion is satisfied. If satisfied, the search will stop on the current node; otherwise it will search the next transition node repeatedly until satisfying the criterion.
P(r,s) =eM(r,s)Smax
Where i, j indicates all the pixels that are in the 8neighborhood of the pixel (i, j) .
ij = aE
ij = aE
(r ,s)
u M
u M
(i, j ) (r ,s)
(r ,s)
(r ,s)
where M
Smax
(u,v)
(u,v)
(u,v)
(u,v)
[ (r ,s)] [(r ,s)]
E=max{En}
We can conclude from(u,vtRh)is formula, the smaller of the pheromone and heuristic measure on the point,
the higher
probability of the search stopped at that point.

An antinspired algorithm for detection of image edge features[6]
The proposed model is based on the fact that an image is composed of a number of pixels, creating a map of cells. A neighborhood is defined for each pixel which identifies where the ants are permitted to move next. Pheromone is a decisive component in ant colony algorithms. Authors have defined two types of pheromone for the problem.
Each ant lays a trail of pheromone typeI as it forages through the 2D map. Each ant is assigned a short term memory , which it uses to remember its last place that it visited also to follow a constraint: Constraint: An ant is not permitted to directly return to its previous cell.
Applying constraint significantly increases the
mobility of the agents. If each ant is influenced by
broken edges. The edges extracted from the above steps provide larger end point information as compared with that provided by Sobel operator.
Edge Improvement
Several discontinuities appear in the image after the application of adaptive thresholding.
The ACO algorithm is used to increase the connectivity of the edges in the image obtained after applying local adaptive thresholding. The steps are as follows:

Initialize the ants position by placing them only at end points.

Initialize the pheromone matrix and calculate the heuristic information using Eq.,
I (x 1, y 1) I (x 1, y
I (x 1, y 1) I (x 1, y
max
any type of pheromone in the environment, it can still be easily entangled in a loop of pheromone laid by it or other agents. To avoid this situation, we define pheromone typeII. This type of pheromone is the component responsible for the decision making process of the ants.
ij
ij I (x, y 1) I (x, y 1),
I (x 1, y) I (x 1,
max
Postprocessing
The threshold value determines the minimum amount of normalized pheromone required for a pixel to be accepted as an extracted feature. To be able to evaluate the features with regard to other

Construction Process: For the ant index 1 : k
Move the kth ant for L steps according to the probabilistic transition matrix.
edge detection methods, a threshold value, T, is
introduced into the procedure.
( ij
(n) )
(ij )


Edge Detection Using Adaptive Thresholding and Ant Colony Optimization[18]
pij (n)
j i
( ij
(n) )
(ij )
Edge Detection using Adaptive Thresholding
In the proposed approach, initially edges are extracted using adaptive thresholding. The connectivity of the edges so obtained is then increased using modified ACO.
Adaptive thresholding typically takes a gray scale or color image as input and, outputs a binary image representing the edge information. For each pixel in the image, a threshold is calculated. If the pixel value is below the threshold it is set to the background value, otherwise it assumes the foreground value. The edges obtained using adaptive thresholding contains some thick edges also therefore a thinning algorithm is implemented for the preprocessing for an efficient end point analysis . The processed image is then analysed to obtain the end point information of the
where (n)ij is the pheromone information value of
the arc linking the node i to the node j, i,j represents the heuristic information for pixel (x,y) for going from node i to node j.

Calculate maximum probability of transition as per the transition rule and move the ant accordingly.

Perform local pheromone update process.

Check whether all ants have moved one step, if yes, perform the global pheromone update.

Check whether the ant can move to the next position by applying the stopping rules, if not, stop the ant.
Stopping rules:

The movement of the ant is stopped when it touches the track already traversed by another ant.

When all the neighboring pixels (8 pixels in 3*3 grid) are already traversed by the ant, then the movement of ant stops.


Decision Process:
The pheromone matrix so produced is used to extract the complete edge trace by applying thresholding.

The edge pixels obtained are combined with the edge pixels obtained by adaptive thresholding to get the complete edge information.
Finally, the results are analyzed using Shanons Entropy function.


ANT COLONY SYSTEM[7]

AS was the first algorithm inspired by real ants behavior. AS was initially applied to the solution of the traveling salesman problem but was not able to compete against the stateofthe art algorithms in the field.
A. General ACS algorithm

Initialize pheromone trails and place M ants on the nodes of AS graph

Repeat until system convergence

For i = 1 to n

For j = 1 to M

Choose the node s to move to, according to the transition probability specified in (1).

Move the antk to the node s



Update the pheromone using the pheromone update formula (3)

Ants change pheromone level of edges by applying local updating rule as described in equation (3).
(i,j) (1) . (i.j,) + . 0 ————(3)
where, 0<<1 is the coefficient representing pheromone evaporation, and n is the number of cities and 0 = (n*Lnn)1, where Lnn is the tour length produced by nearest neighbour heuristic [13]. After all ants complete their cyclic tour, only the globally best ant (i.e. ant belonging to shortest tour) changes trail following global updating rule as given in
equation (4).
(i,j) (1 ) . (i,j) + . (i,j) ——(4)
where, 0<<1 is pheromone decay parameter, Lgb is the length of globally best tour, and
(i,j) = (Lgb)1 , if (i,j) global best tour
0 , otherwise ————–(5)
Global updating rule is similar to a reinforcement learning process as in this case better solutions get higher reinforcement, thus providing high amount of trail to shorter tours.

RECURSIVE ANT COLONY SYSTEM
The Recursive Ant Colony System (RACS) algorithm applies a partitioning scheme to the problem in a manner analogous to the recursive merge sort based on the divide and conquer technique. The algorithm is based on the fact that the efficiency of Ant Colony applications is better for problems of smaller size having less number of cities. This occurs due to the random nature of the algorithm, in which a large number of good random decisions made on weighed choices are required to come together to construct an efficient solution and as the size of the problem increases, so do the number of decisions to be made to generate a single tour. The RACS algorithm partitions the set of all nodes for a problem; say S, into two disjoint sets, say S1 and S2, and then proceeds to find solutions independently for the two subproblems now created by focusing on rducing the lengths of the segments formed by these sets in the original tour, keeping the end points of any new path same as that in the original path. As the search space for these subproblems gets reduced, resulting from the division of the nodes for the original problem, the exploration efficiency and hence the accuracy of the ACS algorithm is much greater for these subproblems. The accuracy of the overall solution obtained by the conjunction of the solutions obtained from these subproblems is upper bounded by the accuracy of the division of the nodes for each subset, which in turn depends upon the accuracy of the initial candidate tour generated. Thus, the RACS algorithm employs a strategy of generating a candidate tour initially using an iterative ACS procedure, followed by partitioning of the tour and recursive implementation of the ACS and Greedy 2 opt(for symmetric TSPs) algorithms on the sub problems created at each recursive level, to further improve the candidate solution initially generated. Recursive ACS is based on Ant Colony System. This technique is used to solve Traveling Salesman Problem. Now the same technique we have applied on Edge Detection & Feature Extraction problem. The proposed ACObased image edge detection approach aims to utilize a number of ants to move on a 2D image for constructing a pheromone matrix, each entry of which represents the edge information at each pixel location of the image.
In edge detection problem before applying RACS we have to apply ACS on the problem, in which first the image has been read. Parameters like: p = 0.0001 .* ones(size(img))
alpha = 10
beta = 0.1
rho = 0.1
phi = 0.05
T=0.01 has been initialized.
Heuristic function has been initialized & calculated. In feature extraction module, color feature has been considered as an extracted feature, in which RACS has been implemented in similar manner as in edge detection using RACS. After calculation of pheromone & heuristic information, histogram technique is used for color feature extraction. After comparing the Feature extraction using ACS & RACS, we concluded that the execution time taken by feature extraction using RACS is far less than that of using ACS.
IV RESULT & DISCUSSIONS
The figure of merit is the useful measure for assessing the performance of edge detectors. This measure uses the distance between all pairs of points corresponding to quantify, with precision, the difference between the contours . The figure of merit
, which assesses the similarity between two contours is defined as:
where NI & NB are the points of edges in the image and ground truth image, respectively, di is the distance between a edge pixel and the nearest edge pixel of the ground truth and is a empirical calibration constant and was used = 1/9. The figure of merit is an indicator of the quality of edge, and reflects the overall behavior of the distances between the edges, being a relative measure, which varies in the range [0,1], where 1 represents the optimal value, i.e., the edges detected coincide with the ground truth.
We have test the algorithm on following image for edge detection

Cameraman

Clown

Lena

Results for algorithm is shown as follows:

Fig: Input Image
Fig: Flat Function Fig: Square Function
Fig: Sine Function Fig: Wave Function

Fig: Clown
Fig: Flat Function Fig: Square Function
Fig: Sine Function Fig: Wave Function

Fig : Lena
Fig: Flat Function Fig: Square Function
Fig: Sine Function Fig: Wave Function
Image 
ACS 
RACS 

Time 
Pixels 
FOM 
Time 
Pixels 
FOM 

Clown 
282 
2020 
0.023 
0.37 
1944 
0.024 
Cameram an 
221 
2282 
0.74 
0.09 
2074 
0.7 
Lena 
685 
2289 
0.07 
0.59 
2161 
0.07 
Image 
ACS 
RACS 

Time 
Pixels 
FOM 
Time 
Pixels 
FOM 

Clown 
282 
2020 
0.023 
0.37 
1944 
0.024 
Cameram an 
221 
2282 
0.74 
0.09 
2074 
0.7 
Lena 
685 
2289 
0.07 
0.59 
2161 
0.07 
Table: Performance Evaluation for ACS & RACS
Above images were tested upon ACS & RACS. It executes extremely fast for RACS. It also performs better in terms of figure of merit for two images.
For feature extraction it executes extremely fast with RACS as compared to ACS.
Fig: Input Image
Thus, RACS also work better for feature extraction. Color feature has been taken for extraction. Above diagram shows no. of different color pixels (R,G,B) has been extracted.
V. CONCLUSION
Ant Colony Optimization algorithm is widely used in edge detection problems. Here we proposed Recursive ant colony optimization algorithm to solve edge detection and feature extraction. Algorithm for edge detection is implemented on three images like clown, cameraman and Lena. Performance is evaluated on parameters like time and figure of measure. RACS performs better as compared to original ACS. RACS also performs better for Feature extraction. The comparison is done on execution speed. Future work is to used other evolutionary techniques in edge detection and compare the result with proposed work.
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