Quest Journals Journal of Software Engineering and Simulation Volume 3 ~ Issue 5 (2017) pp: 24-30 ISSN(Online) :2321-3795 ISSN (Print):2321-3809 www.questjournals.org Research Paper
New Outdoor Image Segmentation Scheme Using Gradual Cluster Elimination and Sewn Gabor Response Dr. Hossein Abbasi and Saeed Saify Received 20 Jan, 2017; Accepted 31 Jan, 2017 Š The author 2017. Published with open access at www.questjournals.org ABSTRACT:The present study is an attempt to customize the color clustering methods for segmentation and object recognition in the outdoor images, using a multi-phase procedure through a multi-resolution platform, called Gradual Cluster Elimination. In all phases, the primary color clusters are detected and then gradually eliminated to allow the smaller clusters emerge into clearer versions of the image. The major clusters are identified using the adaptive statistical method and color histogram analysis. Moreover, the morphology functions are used to eliminate the sub-regions and the weighted graphs are employed to estimate the number of the primary colors and merge the adjacent homogeneous regions. Subsequently, each color region is examined to identify the adjacent homo-chromatic objects with different textures. A new feature, named Sewn Gabor Response is introduced to indicate the texture strength in different directions/resolutions by combining the energies of the detail images applying the Gabor filters bank. This new schem can reduce the complexity of the multi-dimensional clustering and providing acceptable performance improvement. Keywords:Image Segmentation, Outdoor Image, Gradual Cluster Elimination, Sewn Gabor Response.
I.
INTRODUCTION
Outdoor images processing and analysis have recently gained importance in computer vision systems. Computer vision and image processing techniques have found broad applications in various fields such as traffic control [1], security organization [2], automated analysis of the images of cities [3], mobile robots [4], wearable computers designs [5], and assistance of nearly blind or blind people for travel [6]. The increased number of applications of the computer vision enhances the need for the development of this system. Since some devices can only display a limited number of colors, usually due to memory limitations, color image segmentation has become an important issue for machine vision system [3]. Automatic processing of the outdoor images has been one of the most important issues in computer vision system for many years. Due to the special features of the images, outdoor images segmentation as a low level processing is a challenging issue. Some of the most important features are luminance variation and textual variety. The specific features of outdoor images have made object segmentation and recognition more difficult. Some of the problems are luminance variance and dynamic luminance range, color variation and ample detail textures, several patterns, the lack of homogeneity of local or region statistics and complexity and nongeometric features of the object model, the lack of control on luminance conditions of the environment, the presence of small and heterogeneous objects in the images, and the diversity of objects [7]. Although most of the studies have adopted feature-based and cluster-based algorithms for analysing outdoor images, the results of these studies have proved to be far from satisfactory. These studies have suffered from high computational load, extra multi-dimensional clustering, and lack of feasibility, for example, calculation of the optimal threshold in noisy environments, processing real-time tasks [8-10]. Moreover, some adjacent objects with similar colors may form a color region, while the textures are dissimilar [11-13]. Despite using both color and texture features as the most important low-level features of the images, the segmentation quality can be significantly enhanced. However, research concerning this issue is scarce. This study aims to fill this gap by offering a novel color reduction approach that overcomes the above-mentioned drawbacks and reduces the complexity of the multidimensional clustering through providing acceptable segmentation performance improvement and facilitating object recognition.
II.
TYPICAL METHOD
The problems of the clustering algorithms by the aim of appropriate segmentation pre patterns creating in the outdoor images consist of over-segmentation and under-segmentation. Over-segmentation occurs when an *Corresponding Author: Dr. Hossein Abbasi1
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New Outdoor Image Segmentation Scheme Using Gradual Cluster Elimination And.... object is partitioned into several smaller segments, while under-segmentation occurs when just one color is attributed to some single objects [15]. K-means color clustering technique has the ability to reduce memory and transmission bandwidth by maintaining the quality of the image. However, outdoor scenes contain various textures and color variation and for this reason standard k-means algorithm has some limitations during clustering the outdoor scenes. This happens because of the consideration of color and texture details of the images instead of considering the whole image by the standard k-means algorithm. Consequently, uncorrelated data of the images like tiny objects and various texture result in excessive noise generation in k-means algorithm which create the possibility of missegmentation [6]. (I) Proposed Research Special features of the outdoor images have raised some major challenges in low-level analysis of the images, particularly, in their segmentation. As explained earlier, image analysis plays an important role in the quality of machine vision systems and object recognition. This study is an attempt to investigate the outdoor image segmentation challenges and propose a customized method to improve the performance of the initial phase of the system, which is based on segmentation. The customization should be done in order to facilitate the identification of the objects in images. Improving the quality of segmentation in computer vision systems is one of the factors which determines the success of the systems. Finally, the proposed scheme has been evaluated on outdoor images dataset namely BSDS300 and the results have been compared to PRI, NPR, and GCE statistical metrics. (ii) Gradual Cluster Elimination Image segmentation is the process of dividing a given image into homogeneous regions with respect to certain features corresponding to real objects in the actual scene. According to Literature, common color clustering algorithms have some limitations for performing outdoor image segmentation such as wrong identification of images and wrong detection of objects. Rasti et al. has proposed a color reduction procedure based on k-means algorithm for creating 6colored and 9-colored images [15]. By this method, in each level of pyramid using clustering, multi resolution images can recognize important objects and then remove them from the lower levels of the pyramid. A drawback of adopting the multi resolution pyramid is that by applying the smoothing filter, the color and the shape of the image edges will change. This causes the edges of the image to be considered as a new color class. For this reason new customized color clustering algorithms must be offered, which can eliminate current drawbacks. Hence, we proposed a new customized GCE method for creating a 9-color image using Kuwahara smoothing filter which consists of the stages below: 1. Using Kuwahara smoothing filter three blurred versions of the image that have been respectively called, B1, B2 and B3 emerged. Figure 1 represents the three blurred images along with the original one.
Figure 1: The Blurred Versions of an Outdoor Image (a) The Original Image, (b) The Image B1, (c) The Image B2, and (d) The image B3. 2. Using the RGB competitive quantization algorithm, the image B3 (the most blurred version of the image) is divided into three colour clusters. These three clusters correspond to main colours of the images, which have high pixel density. An important point is that there are the numbers of pixels, which are not really similar to these three clusters, or they belong to other objects and their colours are different from the colour *Corresponding Author: Dr. Hossein Abbasi1
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New Outdoor Image Segmentation Scheme Using Gradual Cluster Elimination And....
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of the three clusters but they are forced to be in the clusters. The next phase will be clustering these points separately. We will calculate the Euclidean distance of the points of the image to the three centroids obtained from the previous step. The points, which their Euclidean distance of three centroids is greater than the th-1 threshold, don’t fall into the color clusters and need to be re-clustered again. These points, which are called orphan pixels. We will divide the orphan points into three new clusters to have of 6 color clusters totally (Similar to the method adopted in phase 2). We will compare the image pixels with the obtained centroids of the clusters. Each pixel that shares more similarity to the cluster centroids will be attributed to that cluster. The Euclidean distance of the points of the image B1 to the three centroids obtained from the previous step will be calculated according to the adapted method in phase 4. Those points that their Euclidean distance of three centroids is greater than the th-2 threshold, don’t fall into the color clusters and need to be re-clustered again. The obtained clusters in the image which were divided into 9 classes are more suitable for segmentation than those classes which came out of the initial division of the image. For a better comparison, in the figure, we have used a different color palette and flashy colors for pseudo-coloring.
(iii) Eliminating Sub-Regions Using Morphology Functions The process of image refining is shown in the Figure 2. In part (a), a typical image comprises three main clusters. Each cluster contains the sub regions of the remaining clusters.The steps of the method:
Figure 2 Refining of an outdoor image: (a) Original image, (b) Clustered image, and (c) Refined image 1. 2. 3. 4. 5.
The features of the sub regions (the region that are less than the given threshold) are documented. The registered sub regions from the step 1 to the cluster are added in order to fill the pores with respect to each cluster. The sub-regions that are out of the continuity place of the cluster are eliminated through the opening function [14]. In this study, 5 cm Disk structuring element were used. The obtained points are labelled or marked according to the related cluster. Step 2 might be repeated for processing the other clusters.
(iv) Merging Regions Using Region Adjacency Graph First, each graph node shares the color of one image region. The nodes belonging to the adjacent regions are connected to one another. The edge weight between two nodes is equal to the Euclidean distance of two adjacent regions in CIE-lab color space. Second, after creating the adjacency graph, the weight of the edges is measured. The edges with the lowest weight represent the adjacent regions image in which the colors are very similar. If the weight of the edge is less than the given threshold, two regions are merged to create a new region. Similarly, the two nodes matching the two regions in the graph structure are merged and the color of the region as the color feature of the new node is recorded to update the weight of the edges relating to one of the two nodes. *Corresponding Author: Dr. Hossein Abbasi1
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New Outdoor Image Segmentation Scheme Using Gradual Cluster Elimination And.... Last, the process is continued up to the lowest weight of the edge in the graph (equivalent to the lowest visual difference between the two adjacent color regions) is more than the given threshold. In other words, there no two similar regions exist. The threshold percentage of the maximum Euclidean in CIE-lab color space is set. The more percentage is, the more regions are prone to be merged to create bigger regions(see Figure 3).
Figure 3Colors clustered with adjacency graph method in BSDS data set. (a) Original image, (b) Clustered image, (c) Refined image, and (d) Merging Regions Using Region Adjacency Graph (v) Sewn Gabor Response This study introduces a new hybrid feature which is called sewn Gabor response (SGR). The term sewn refers to the calculation method of SGR feature, as the different responses of the Gabor filters are combined weightily (sewn), and create the feature. The general idea is to show the relative strength of different Gabor responses in the form of a weighted number. The numerical computation of the features is as follows: 1. The frequency signature vector of all pixels are firstly calculated[12], Element đ?‘‰đ?‘– , in the set (the vector). V of each pixel stands for the energy of that pixel in resolution and direction of i. It is obtained after applying the Gabor filters. The element V1 and element V16 are assumed to be in the direction of 0° of the first octave in the direction of 135° of the fourth octave, respectively. 2. The mean of vectors V for all pixels are calculated and is called G. The đ?‘– đ?‘Ąâ„Ž member of the vector (đ??şđ?‘– ) is the result of applying the Gabor filter i (related to a specific resolution and direction) on the image pixels. 3. Based on the members of vector G, vector V is given a threshold value and converted to zero and one to obtain a set of vectors T. The vector T is obtained by thresholding the vector V according to the Equation. Ti = {10
đ?‘Łđ?‘– ≤đ??şđ?‘–
vi ≼ G i For measuring SGR, the same procedure used for LBP is adopted. Equation shows the way in which SGR feature is calculated for a pixel with a gray-tone energy vector. Since the energy level of each pixel in different directions and resolution is shown using the gray energy-tone vector T, by multiplying the elements of the vector T by the integer powers of 2 and summing them together, a set of weighted summation of the energies is obtained for different resolutions and region patterns which are unique. đ?‘–−1 SGR = ∑16 đ?‘–=1 đ?‘‡đ?‘– 2
For example, if TSR of a pixel is equal to 8618, by dividing it by the powers of 2, we have: 8618=2+8+32+128+256+8192. The pixel has high level of energy in the octave 0 and direction 45. The octave 0, direction 135, the octave 1 and direction 45, the octave 1 and direction 135, the octave 2, direction 0 and the octave 3, direction 135 and in the rest of the precisions and directions has no considerable energy. Since the numbers are uniquely divided by the integer powers of 2, the TSR values for the summation of different energies in different directions and precisions are unique. Finally, each pixel has a SGR number. Obviously, clustering a set of number is easier than the multidimensional clustering of the signature frequency vectors. Due to the approximate value of the threshold, such clustering is less precise than the Gabor main energy vectors.However, the images of this test show that this *Corresponding Author: Dr. Hossein Abbasi1
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New Outdoor Image Segmentation Scheme Using Gradual Cluster Elimination And.... error is negligible. If TSR is zero, the pixels have not too much of energy in different directions or resolutions. Thus, the pixels correspond to the flat regions of the image.
III.
Evaluation of the Segmentation Algorithm
The present study evaluates the quality of the proposed algorithm on three indexes: Probabilistic Rand Index (PRI) Metric: The value of PRI is between zero and one if image S is different from the reference image (image S has one segment and the images of the reference set include the segments of the size of a pixel), the amount of PRI would be zero and in the case of the entire similarity between S and reference segments, the value of PRI would be equal to 1. A measure of the probable RI or PRI is defined as it is shown in Equation. PR(S,S 1…K ) =
1 ( N2 )
∑ᵢ,ϳ ᵢ≠ϳ [P Lᵢ = Lϳ I Lᵢ = Lϳ + P Lᵢ ≠ Lϳ + I Lᵢ ≠ Lϳ ]
Normalized Probabilistic Rand Index (NPR) Metric: the standardized version of the PR is introduced under the name of the NPR. This extends the range of variations. Equation shows how the NPR index is calculated. Index −Expected Index
NPR= Maximum Index −Expected Inde Global Consistency Error (GCE) Metric: It is one of the most important metrics for the evaluation of quality of a region-oriented segmentation, general compatibility error or GCE. This metric specifies the extent to which segmentation is the modified version of the other. Equation shows the way in which the global consistency error is calculate. 1 GCE (S1 , S2 ) = min {∑i E S1, S2, Pi , ∑i E(S2, S1, Pi )} n
The proposed scheme in this study is effective for the images with large clusters (such as landscape or scenery), while the Berkeley dataset includes different images such as animals, human faces and urban landscapes that are not appropriate for the proposed schem. For example by selecting 50 images of BSDS dataset 300 which have large clusters and is a kind of landscape, PRI metric will increase to (0. 86). Figure 4 show Some examples of color images of BSDS300.
*Corresponding Author: Dr. Hossein Abbasi1
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New Outdoor Image Segmentation Scheme Using Gradual Cluster Elimination And.... Figure 4Some examples of color images of BSDS300 data set which are clustered based on. (a) The Original Image, (b) The Ground Truth Image, (c) The Color Clustered Image by k-means, (d) The 9- Color Clustered by Hue Method, and (e) The Color Clustered Image by TSR Method Table 1: Comparison of the standard clustering (k-means) and the proposed scheme performanceon the Figure 4BSDS 300 dataset Clustering algorithm
GCE Method
1
Standard clustering (k-means) 0.72
0.76
Proposed Scheme (GCE+SGR) 0.82
2
0.70
0.75
0.81
3
0.68
0.76
0.83
NPR
4 1
0.71 0.32
0.79 0.44
0.84 0.52
GCE
2 3 4 1
0.27 0.33 0.31 0.36
0.40 0.44 0.46 0.29
0.50 0.57 0.59 .018
2 3 4
0.38 0.37 0.41
0.27 0.25 0.24
0.19 0.19 0.17
PRI
Number
In order to offer appropriate patterns of segmentation, over-segmentation/under-segmentation in color clustering of the outdoor images is considered callenging. On one hand, the big clusters are partitioned into some segments and on the other hand, the small clusters are overlooked. Such challenges can be eliminated by color cluster elimination based algorithm. The reason is that the algorithm focuses on the bigger clusters at the top level of the pyramid, and then in the next phase, elimination of the important clusters allows the small clusters to be emerged. Therefore, limiting the number of clusters in the first phase helps overcome the oversegmentation problem. Moreover, focusing on the smaller cluster in the second phase prevents form the undersegmentation problem. Features of the color cluster elimination based algorithm are as follows: First, Effective Management of the details: Small objects and texture details do not interrupt the clustering process because they are removed in the initial phase through the smoothing process to be emerged later. The use of the multi resolution pyramid improves the result noticeably. In the absence of the pyramid, the segmentation error increases up to 6% [15]. Using the pyramid, changes in the histogram graph showing the distance of the pixels from the centroid are less, in other words, using the pyramid, the similar color are merged as a result the homo-chromatic-pixels in the cluster are evenly distributed. This in turn reduces the errors of the segmentation algorithm in dealing with the shades of the same color and texture of the outdoor images. Second, Resistant to Noise: the multi-resolution platform, which is used for the algorithm decreases the effect of noise on the segmentation quality. If the image has some noise, using the smoothing filter especially in the first phase that big clusters are created the effect of noise will be declined. In the second phase, in the process of estimating the number of the color clusters, only primary colors will be considered. This will lessen the noise effect on the final color clusters.The multi resolution context used for the algorithm prevents the negative influence of noise on the segmentation of color clustering.
IV.
CONCLUSION
This study was an attempt to examine the segmentation of outdoor scenes for the purpose of object recognition. A multi-phase method called GCE in the context of multi-resolution images was proposed. In each phase of the method, the important color clusters related to the large objects of the images were recognized and gradually eliminated to allow the smaller clusters emerge in clearer versions. The major clusters in each phase were detected using the statistical methods based on the color histograms. In addition, the morphological functions were used to eliminate the existing sub regions which remain after clustering within the large regions. The weighted Graph theory was employed to estimate the primary colors of the images, and merge the similar adjacent regions. The emerged segments applying the GCE algorithm are merely based on the color features of images. However, in color clustering, two diverse homo-chromaticobjects which have different textures may be located next to each other and form a cluster. This explains why each of the emerged color regions needs to be re-examined and in the case they do not share identical texture in various parts, they need to be partitioned into some different sub regions. A new feature of Sewn gabor Response method was introduced to indicate the combined energies in different directionsand resolution. For texture analysis of a color region, the amount of SGR of all pixels belonging to the region are calculated. This method helps reduce the computational load of *Corresponding Author: Dr. Hossein Abbasi1
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New Outdoor Image Segmentation Scheme Using Gradual Cluster Elimination And.... multi-dimensional clusters. Finally, the proposed scheme has been evaluated on outdoor images dataset namely BSDS300 and the results have been compared to PRI, NPR, and GCE statistical metrics. The comparative tables show premising performance of this method for the outdoor images segmentation.
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*Corresponding Author: Dr. Hossein Abbasi1
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