The week's pick
Reseach Article
Extracted Pixels Similarity Features (EPSF) using Interactive Image Segmentation Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 136 - Number 2 |
| Year of Publication: 2016 |
| Authors: Alhaji Sheku Sankoh, Agus Zainal Arifin, Arya Yudhi Wijaya |
10.5120/ijca2016908236
|
Alhaji Sheku Sankoh, Agus Zainal Arifin, Arya Yudhi Wijaya . Extracted Pixels Similarity Features (EPSF) using Interactive Image Segmentation Techniques. International Journal of Computer Applications. 136, 2 ( February 2016), 5-12. DOI=10.5120/ijca2016908236
Abstract
Semi-automatic image segmentation methods are among the segmentation methods that are used to achieve high quality segmentation result. These techniques are said to be interactive because their processes required input from their users to distinguish between the foreground and background of the image via color makers. Therefore color and texture features are an important aspect for the improvement of those methods in order to achieve success. The reason is that, the addition of a vast range of color features can guide the region merging process to achieve accurate results. In this paper, we proposed a new interactive image segmentation method based on extracted pixels similarity features with the aid of color parameters comprising of seven features. These are namely: Red, Green, Blue, Hue, Saturation, Value and Texture Features. In our method, the initial step is to pre-segment the image. The next is the extraction of the color features from the image. Finally our method then merge all the pre-segmented regions of the image background to extract the contour. Our proposed method has successfully been implemented and achieve good quality segmentation results by measuring the similarity between a region and its neighboring regions. In this method, the image regions are acquired by means of a pre-segmentation process via mean-shift. The region merging process was carried out based on the highest similarity between regions. Thus, the regions were merged with their neighboring regions based on the fact that the highest similarity criteria was achieved. From the various experiments that were performed on the nine test images, it is evident that our proposed method achieved an average of 98.99% from all experiment carried out on the nine test images, when compared with their ground truth. That shows that our proposed method produced better segmentation results than the Graph Cut and the Dimension-free Directional Filter Bank Thresholding and Multistage Adaptive Thresholding respectively. Finally, our proposed method have been proved to be robust enough to segment both color and greyscale images and based on the results produced it can be concluded that our proposed method have achieved good quality segmentation results for both color and greyscale images.
References
- J. Ning, L. Zhang, D. Zhang, and C. Wu, “Interactive image segmentation by maximal similarity based region merging,” Interact. Imaging Vis., vol. 43, no. 2, pp. 445–456, Feb. 2010.
- Costas Panagiotakis, HarrisPapadakis, EliasGrinias, and NikosKomodakis, “Interactive image segmentation based on synthetic graph coordinates,” ScoenceDirect, vol. Volume 46, no. Issue 11, p. Pages 2940–2952, 2013.
- Pedro F. Felzenszwalb and Daniel P. Huttenlocher, “Efficient Graph-Based Image Segmentation,” Int. J. Comput. Vis., vol. Vol. 59, no. No. 2, p. pp 167–181, Sep. 2004.
- Frank Heckel, Olaf Konrad, Horst Karl Hahn, and Heinz-OttoPeitgen, “Interactive 3D medical image segmentation with energy-minimizing implicit functions,” Virtual Real. Braz. Comput. Biol. Med. 3D Media ContentCultural Herit., vol. 35, no. 2, pp. 275–287, Apr. 2011.
- B. Chen, Q. Zou, and Y. Li, “A new image segmentation model with local statistical characters based on variance minimization,” Appl. Math. Model., vol. 39, no. 12, pp. 3227–3235, Jun. 2015.
- Ming Zhang, Ling Zhangb, and H.D. Cheng, “A neutrosophic approach to image segmentation based on watershed method,” Spec. Sect. Stat. Signal Array Process., vol. 90, no. 5, pp. 1510–1517, May 2010.
- Gururaj P Surampalli, Dayanand J, and Dhananjay M, “An Analysis of Skin Pixel Detection using Different Skin Color Extraction Techniques,” Int. J. Comput. Appl. 0975 - 8887, vol. 54, Sep. 2012.
- Dunn D and Higgins WE., “Optimal Gabor filters for texture segmentation,” IEEE Trans. Image Process., vol. 4, no. Issue 7, pp. 947 – 964, Year 1995.
- Meer, P and Georgescu, B., “Edge detection with embedded confidence,” Pattern Anal. Mach. Intell. IEEE Trans., vol. 23, no. 12, pp. 1351 – 1365, Aug. 2002.
- Doring Comaniciun and Peter Meer, “Mean Shift: A Robust Approach Towards Feature Space Analysis.,” IEEE, vol. 24, no. 5, pp. 603 – 619, May 2002.
- Birchfield, S., “Elliptical head tracking using intensity gradients and color histograms,” IEEE Conf. Comput. Vis. Pattern Recognit., pp. 232 – 237, Jun. 1998.
- Liwei Wang, Yan Zhang, and Jufu Feng, “On the Euclidean Distance of Images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 8, pp. 1334 – 1339, Aug. 2005.
- Donald G Bailey, “An Efficient Euclidean Distance Transform,” 10th Int. Workshop IWCIA 2004, vol. 3322, no. 2, pp. 394–408, Dec. 2004.
- Qiong Yang, Xiaoou Tang, Chao Wang, and Zhongfu Ye, “Progressive Cut: An Image Cutout Algorithm that Models User Intentions,” Multimed. IEEE, vol. 14, no. 3, pp. 56–66, Sep. 2007.
- C. Panagiotakis, H. Papadakis, E. Grinias, N. Komodakis, P. Fragopoulou, and G. Tziritas, “Interactive image segmentation based on synthetic graph coordinates,” Pattern Recognit., vol. 46, no. 11, pp. 2940–2952, Nov. 2013.
- T. N. A. Nguyen, J. Cai, J. Zheng, and J. Li, “Interactive object segmentation from multi-view images,” J. Vis. Commun. Image Represent., vol. 24, no. 4, pp. 477–485, May 2013.
- University of California and Berkeley Segmentation Dataset (BSDS), “Berkeley Segmentation Dataset and Benchmark,” Comput. Vission Group, vol. BSDS500, no. 1, Jan. 2013.
- Agus, Z A. and Akira A, “Image segmentation by histogram thresholding using hierarchical cluster analysis,” Pattern Recognit. Lett., vol. 27, no. 13, pp. 1515–1521, Oct. 2006.
- Rarasmaya Indraswari, Agus Zainal Arifin, Dini Adni Navastara, and Nasar Jawas, “Teeth Segmentation on Dental Panoramic Radiograph using Dimension-free Directional Filter Bank Thresholding and Multistage Adaptive Thresholding,” Inf. Commun. Technol. Syst. ICTS 2015 Int. Conf., pp. 49 – 54, Sep. 2015.
Index Terms
Keywords