Abstract
Image segmentation for object detection is one of the most fundamental problems in computer vision, especially in object-region extraction task. Most popular approaches in the segmentation/object detection tasks use sliding-window or super-pixel labeling methods. The first method suffers from the number of window proposals, whereas the second suffers from the over-segmentation problem. To overcome these limitations, we present two strategies: the first one is a fast algorithm based on the region growing method for segmenting images into homogeneous regions. In the second one, we present a new technique for similar region merging, based on a three similarity measures, and computed using the region adjacency matrix. All of these methods are evaluated and compared to other state-of-the-art approaches that were applied on the Berkeley image database. The experimentations yielded promising results and would be used for future directions in our work.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Krahenbuhl, A.: Segmentation et analyse géométrique: application aux images tomodensitométriques de bois. Thesis, Université de Lorraine (2014)
Peng, B., Zhang, L., Zhang, D.: Automatic image segmentation by dynamic region merging. IEEE Trans. Image Process. 20(12), 3592–3605 (2011)
Hedberg, H.: A survey of various image segmentation techniques. Department of Electroscience, Box 118 (2010)
Fox, V., Milanova, M., Al-Ali, S.: A hybrid morphological active contour for natural images. Int. J. Comput. Sci. Eng. Appl. 3(4), 1–13 (2013)
Niyas, S., Reshma, P., Thampi, S.M.: A color image segmentation scheme for extracting foreground from images with unconstrained lighting conditions. Intelligent Systems Technologies and Applications 2016. AISC, vol. 530, pp. 3–19. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47952-1_1
Heisele, B.: Visual object recognition with supervised learning. IEEE Intell. Syst. 18(3), 38–42 (2003)
Erhan, D., Szegedy, C., Toshev A., Anguelov D.: Scalable object detection using deep neural networks. In: CVPR (2014)
Bappy, J.H., Roy-Chowdhury, A.: CNN based region proposals for efficient object detection. In: IEEE International Conference on Image Processing (ICIP) (2016)
Yan, J., Yu, Y., Zhu, X., Lei, Z., Li, S.Z.: Object detection by labeling superpixels. In: CVPR (2015)
Blasiak, A.: A Comparison of image segmentation methods. Thesis in Computer Science (2007)
Pantofaru, C., Hebert, M.: A comparison of image segmentation algorithms. Carnegie Mellon University (2005)
Robinson, D.J., Redding, N.J., Crisp, D.J.: Implementation of a fast algorithm for segmenting SAR imagery. Scientific and Technical report, Defense Science and Technology Organization, Australia (2002)
Rosenberger, C., Chabrier, S., Laurent, H., Emile, B.: Unsupervised and supervised image segmentation evaluation. Adv. Image Video Segm. 29(1), 365–393 (2006)
Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recognit. 29, 1335–1346 (1996)
Li, S., Wu, D.O.: Modularity-based image segmentation. IEEE Trans. Circ. Syst. Video Technol. 25(4), 570–581 (2015)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)
Rao, S.R., Mobahi, H., Yang, A.Y., Sastry, S.S., Ma, Y.: Natural image segmentation with adaptive texture and boundary encoding. In: Zha, H., Taniguchi, R., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5994, pp. 135–146. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12307-8_13
Browet, A., Absil, P.-A., Van Dooren, P.: Community detection for hierarchical image segmentation. In: Aggarwal, J.K., Barneva, R.P., Brimkov, V.E., Koroutchev, K.N., Korutcheva, E.R. (eds.) IWCIA 2011. LNCS, vol. 6636, pp. 358–371. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21073-0_32
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1271–1283 (2010)
Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2004). https://doi.org/10.1007/978-3-662-05088-0
Cour, T., Benezit, F., Shi, J.: Spectral segmentation with multiscale graph decomposition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, vol. 2, pp. 1124–1131. IEEE (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Amrane, A., Meziane, A., Boulkrinat, N.E.H. (2018). Object Detection in Images Based on Homogeneous Region Segmentation. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-92058-0_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92057-3
Online ISBN: 978-3-319-92058-0
eBook Packages: Computer ScienceComputer Science (R0)