IMAGE SEGMENTATION AND OBJECT EXTRACTION USING BINARY PARTITION TREE

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1 ISSN : Vol. 3, No. 1, January-June 2012, pp IMAGE SEGMENTATION AND OBJECT EXTRACTION USING BINARY PARTITION TREE Uvika 1 and Sumeet Kaur 2 1 Student, YCoE, Patiala uvikataneja01@gmail.com 2 Asst. Prof. YCoE, Patiala purbasumeet@yahoo.co.in ABSTRACT This paper proposes a morphology based edge detection method and Binary partition tree for object extraction. Starting from an initial segmentation result generated by applying multi-iteration, multi-thresholding watershed algorithm and then applied Morphology based edge detection method. The proposed method solved the problem of undesirable over-segmentation results produced in the images and final edge detection result is one closed boundary per actual region in the image. Then intensity based region merging scheme can be exploited to merge the regions. The merging sequence can be efficiently recorded by BPT, to represent the final segmentation result with a small number of segmented regions. Experimental results demonstrate that the proposed approach can obtain a better segmentation performance as compared to existing approach of BPT from the perspective of object extraction. Keywords: BPT, Edge detection, Image segmentation, Object Extraction, Watershed. 1. INTRODUCTION Segmentation is the process of separating a digital image into different segments. So that image can be more simplify, understandable and helpful to analyzing. Image segmentation provides the labels to differentiate the boundaries of different objects on the basis of pixel intensity. Starting from initial segmentation results, for this edge detection based image segmentation method has been used. In the proposed algorithm initial segmentation results have been obtained by applying multi-iteration, multi-thresholding watershed algorithm and then applied Morphology based edge detection method for detecting and defining the boundaries and that regions are contained within these edges of the objects. Morphology refers to the study of forms, structures and algebraic arithmetic operators. For this various morphological operations has been used to maintain good edge information in images and the edges we obtained have no broken lines on entire image. Also, the proposed method solved the problem of undesirable over-segmentation results produced in the images. Binary partition tree (BPT) was introduced to systematically represent the hierarchical segmentation of an image in an efficient way. [1] Binary partition tree created in such a way that tree represents the nodes containing salient image contents and those nodes are selected from the BPT to represent a final and efficient segmentation result with a small number of segmented regions for the purpose of object extraction. 2. BINARY PARTITION TREE Binary partition tree (BPT) was introduced in [11] to systematically represent the hierarchical segmentation of an image in an efficient way. Starting from an initial segmentation result generated by applying multiiteration, multi-thresholding watershed algorithm and then applied Morphology based edge detection method. From the initial segmentation results, we get n initially segmented regions or objects, and then Intensity based merging start to find each object and its nearest neighbour. For this we find the RGB mean value of each object, and then find the intensity difference between each object and its neighbours. Merging continue in the same way in each iteration. In the proposed approach we have used the weighting method in which merging process will continue until it will reach the level of defined threshold value and area threshold value. During the region merging process, a BPT is constructed to record the whole merging sequence and to cluster the closely related regions. In BPT, there are n number of nodes has been used in which each leaf node represents each initially segmented region and each non-leaf node represents the newly generated region. Each non-leaf node has two children nodes which represent the two adjacent regions to be merged, and the root node represents the entire image regions. An example is shown in Fig 1. The original image Sign board is shown in Fig. 1 (a). And its initial segmentation result is shown in Fig. 1(b), in which segmented regions are shown. Fig. 1(c) shows the final segmentation result.

2 148 IJCSC Fig. 1(d) shows the generated BPT, as the tree represents a large set of regions at different levels which contains a total of 127 nodes spanning across 15 levels Flowchart for Proposed Algorithm Figure 1: The Proposed BPT Generation Process. (a) Original Image Sign Board; (b) its Initial Segmentation Result; (c) its Final Segmentation Result; (d) its Corresponding BPT 3. IMPLEMENTATION 3.1. Proposed Algorithm Step 1: Read the RGB image of size rxc. Step 2: Convert the image into a Gray scale image. Step 3: Convert the gray scale image into binary image using multi-iteration, multi-thresholding watershed algorithm. Step 4: Detect the edges of image using Morphology based Edge detection method. Step 5: BPT (binary partition tree) generated to cluster the closely related regions. Step 6: Intensity based merging start to find each object and its nearest neighbor. Step 7: Merging starts for all the objects in image defining threshold value and area threshold value of each object. Step 8: Find (Pixel color values) RGB mean value of each object. Step 9: Find the intensity difference between each object and its neighbors. The element with the minimum intensity difference is merged with the object. Step 10: Merging continue in the same way in each iteration until we reach the level of defined threshold value and area threshold value. Figure 2: The Flowchart of the Proposed Algorithm 4. EXPERIMENTAL RESULTS To evaluate the segmentation performance from perspective of object extraction, we used the performance measure approach proposed in [1]. The segmentation performance measure is calculated based on the comparison between the manually segmented ground truth A for object and the segmentation result S generated by the proposed algorithm. Assume there are n S and n G regions in the segmentation result and the ground truth, respectively. We introduce the latter condition to ensure that at least one region is selected to compose R obj for some under-segmentation results. The denominator in below Equation is a regularization term of region number, which actually penalizes the over-segmentation result as compared with the ground truth, and the penalization degree is controlled by the adjusting coefficient. [1] Then we define the segmentation performance measure as Robj A / Robj A P(S, A) = 1/ x [max( n n + 1,1)] S Segmented results generated for many test images from [1] different categories such as flower, bird, sign board etc. Are shown in Figure 3 in which each one original figure is followed by its human segmented ground truth image and its initial segmentation result image. After that segmentation results image has been shown with the existing algorithms results images taken from [1]. Experimental results generated by proposed algorithm shows better segmentation results as compare G

3 Image Segmentation and Object Extraction Using Binary Partition Tree 149 to existing approach [1]. The performance measure achieved using our approach is somewhat lower than existing approach [1] for the image 12 in Figure 3, in which some part of background are extracted with the main object. Figure 3: Se]gmentation Results of Some Images and Comparison with Existing Approach [1] 5. CONCLUSION In this paper efficient image segmentation and object extraction has been presented in which firstly initial segmentation result has been generated which removed the problem of undesirable over-segmentation results produced in the images and results produced is one closed boundary per actual region in the image. The intensity based merging sequence of regions can be efficiently recorded by BPT, to represent a meaningful and efficient segmentation result with a small number of segmented regions.

4 150 REFERENCES [1] Zhi Liu, Liquan Shen, Zhaoyang Zhang, Unsupervised Image Segmentation Based on Analysis of Binary Partition Tree for Salient Object Extraction, ELSEVIER, Signal Processing 91 (2011) pp [2] Sreenath Rao Vantaram (1) and Eli Saber, An Adaptive Bayesian Clustering and Multivariate Region Merging Based Technique for Efficient Segmentation of Color Images, IEEE, , [3] Rui Huang, Nong Sang, Dapeng Luo, Qiling Tang, Image Segmentation via Coherent Clustering in L/a/b/ Color Space, ELSEVIER, Pattern Recognition Letters 32 (2011) pp [4] De Montréal, A De-Texturing and Spatially Constrained K-Means Approach for Image Segmentation Max Mignotte, ELSEVIER, Pattern Recognition Letters 32 (2011) pp [5] Shihu Zhu, Edge Detection Based on Multi-Structure Elements Morphology and Image Fusion, IEEE, , [6] C. Naga Raju, S. Naga Mani, G. Rakesh Prasad, S. Sunitha, Morphological Edge Detection Algorithm Based on Multi-Structure Elements of Different Directions, International Journal of Information and Communication Technology Research 1 No. 1, May IJCSC [7] Muthukannan. K, Merlin Moses. M, Color Image Segmentation Using K-means Clustering and Optimal Fuzzy C-Means Clustering, Proceedings of the International Conference on Communication and Computational Intelligence [8] Zhiding Yu, Oscar C. Au, Ruobing Zou, Weiyu Yu, Jing Tian, An Adaptive Unsupervised Approach Toward Pixel Clustering and Color Image Segmentation, ELSEVIER, Pattern Recognition 43 (2010) pp [9] Huihai Lu, John C. Woods and Mohammed Ghanbari, "Binary Partition Tree for Semantic Object Extraction and Image Segmentation, IEEE Transactions on Circuits and Systems for Video Technology, 17, No. 3, March [10] J.A. Jiang, C.L. Chuang, Y.L. Lu and C.S. Fahn, Mathematical-Morphology-Based Edge Detectors for Detection of thin Edges in Low-Contrast Regions, IET Image Process., 2007, 1, (3), pp [11] Philippe Salembier, Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and Information Retrieval, IEEE Transactions on Image Processing, 9, No. 4, April [12] Malay K. Kundu, Bhabatosh Chanda and Y. Vani Padmaja, A Multiscale Morphologic Edge Detector. Pattern Recognition, 31, No. 10, pp , 1998.

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