Abstract
Hierarchical image segmentation provides a set of image segmentations at different detail levels in which coarser details levels can be produced by simple merges of regions from segmentations at finer detail levels. However, many image segmentation algorithms relying on similarity measures lead to no hierarchy. One of interesting similarity measures is a likelihood ratio, in which each region is modelled by a Gaussian distribution to approximate the cue distributions. In this work, we propose a hierarchical graph-based image segmentation inspired by this likelihood ratio test. Furthermore, we study how the inclusion of hierarchical property have influenced the computation of quality measures in the original method. Quantitative and qualitative assessments of the method on three well known image databases show efficiency.
The authors are grateful to PUC Minas, CNPQ, CAPES and FAPEMIG for the partial financial support of this work.
Chapter PDF
Similar content being viewed by others
References
Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: CVPR, June 2007
Alpert, S., Galun, M., Brandt, A., Basri, R.: Image segmentation by probabilistic bottom-up aggregation and cue integration. PAMI 34(2), 315–327 (2012)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. PAMI 33, 898–916 (2011)
Beucher, S.: Watershed, hierarchical segmentation and waterfall algorithm. In: Proceedings of the 2nd International Symposium on Mathematical Morphology and Its Applications to Image Processing, ISMM 1994, Fontainebleau, France, September 1994, pp. 69–76 (1994)
Calderero, F., Marques, F.: Region merging techniques using information theory statistical measures. Trans. Img. Proc. 19(6), 1567–1586 (2010)
Cousty, J., Najman, L.: Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 272–283. Springer, Heidelberg (2011)
Cousty, J., Najman, L.: Morphological floodings and optimal cuts in hierarchies. In: ICIP, pp. 4462–4466 (2014)
Cousty, J., Najman, L., Kenmochi, Y., Guimarães, S.: New characterizations of minimum spanning trees and of saliency maps based on quasi-flat zones. In: Benediktsson, J.A., Chanussot, J., Najman, L., Talbot, H. (eds.) ISMM 2015. LNCS, vol. 9082, pp. 205–216. Springer, Heidelberg (2015)
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59, 167–181 (2004)
Guigues, L., Cocquerez, J.P., Men, H.L.: Scale-sets image analysis. IJCV 68(3), 289–317 (2006)
Guimarães, S.J.F., Cousty, J., Kenmochi, Y., Najman, L.: A hierarchical image segmentation algorithm based on an observation scale. In: Gimel’farb, G., Hancock, E., Imiya, A., Kuijper, A., Kudo, M., Omachi, S., Windeatt, T., Yamada, K. (eds.) SSPR & SPR 2012. LNCS, vol. 7626, pp. 116–125. Springer, Heidelberg (2012)
Guimarães, S.J.F., Patrocínio Jr., Z.K.G.: A graph-based hierarchical image segmentation method based on a statistical merging predicate. In: Petrosino, A. (ed.) ICIAP 2013, Part I. LNCS, vol. 8156, pp. 11–20. Springer, Heidelberg (2013)
Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI 26(5), 530–549 (2004)
Morris, O., Lee, M.J., Constantinides, A.: Graph theory for image analysis: an approach based on the shortest spanning tree. IEE Proceedings F (Communications, Radar and Signal Processing) 133(2), 146–152 (1986)
Najman, L.: On the equivalence between hierarchical segmentations and ultrametric watersheds. JMIV 40, 231–247 (2011)
Nock, R., Nielsen, F.: Statistical region merging. PAMI 26(11), 1452–1458 (2004)
Peng, B., Zhang, D., Zhang, D.: Automatic image segmentation by dynamic region merging. IEEE Trans. on Image Processing 20(12), 3592–3605 (2011)
Rother, C., Kolmogorov, V., Blake, A.: “grabcut”: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)
Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans. Comput. 20, 68–86 (1971)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Guimarães, S.J.F., do Patrocínio, Z.K.G., Kenmochi, Y., Cousty, J., Najman, L. (2015). Hierarchical Image Segmentation Relying on a Likelihood Ratio Test. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9280. Springer, Cham. https://doi.org/10.1007/978-3-319-23234-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-23234-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23233-1
Online ISBN: 978-3-319-23234-8
eBook Packages: Computer ScienceComputer Science (R0)