Abstract
Image segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.
References
[1] Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence 34(11), 2274–2282 (2012)10.1109/TPAMI.2012.120Search in Google Scholar PubMed
[2] Adão, M.M., Guimarães, S.J.F., Patrocínio, Z.K.: Evaluation of scale-aware realignments of hierarchical image segmentation. In: Iberoamerican Congress on Pattern Recognition. pp. 141–149. Springer (2018)10.1007/978-3-030-13469-3_17Search in Google Scholar
[3] Angulo, J., Jeulin, D.: Stochastic watershed segmentation. ISMM 1, 265–276 (2007)Search in Google Scholar
[4] Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE PAMI 33(5), 898–916 (2011)10.1109/TPAMI.2010.161Search in Google Scholar PubMed
[5] Chan, T., Zhu, W.: Level set based shape prior segmentation. In: CVPR. vol. 2, pp. 1164–1170 (2005)Search in Google Scholar
[6] Chang, C.I.: Hyperspectral imaging: techniques for spectral detection and classification, vol. 1. Springer Science & Business Media (2003)Search in Google Scholar
[7] Chen, F., Yu, H., Hu, R., Zeng, X.: Deep learning shape priors for object segmentation. In: CVPR. pp. 1870–1877 (2013)10.1109/CVPR.2013.244Search in Google Scholar
[8] Chen, Y., Dai, D., Pont-Tuset, J., Van Gool, L.: Scale-aware alignment of hierarchical image segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 364–372 (2016)10.1109/CVPR.2016.46Search in Google Scholar
[9] Cousty, J., Najman, L.: Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts. In: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing. pp. 272–283. Springer (2011)10.1007/978-3-642-21569-8_24Search in Google Scholar
[10] Cousty, J., Najman, L., Kenmochi, Y., Guimarães, S.: Hierarchical segmentations with graphs: quasi-flat zones, minimum spanning trees, and saliency maps. Journal of Mathematical Imaging and Vision 60(4), 479–502 (2018)10.1007/s10851-017-0768-7Search in Google Scholar
[11] Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. IEEE (2009)10.1109/CVPR.2009.5206848Search in Google Scholar
[12] Fankhauser, P., Bloesch, M., Rodriguez, D., Kaestner, R., Hutter, M., Siegwart, R.: Kinect v2 for mobile robot navigation: Evaluation and modeling. In: Advanced Robotics (ICAR), 2015 International Conference on. pp. 388–394. IEEE (2015)10.1109/ICAR.2015.7251485Search in Google Scholar
[13] Fehri, A., Velasco-Forero, S., Meyer, F.: Automatic selection of stochastic watershed hierarchies. In: 2016 24th European Signal Processing Conference (EUSIPCO). pp. 1877–1881. IEEE (2016)10.1109/EUSIPCO.2016.7760574Search in Google Scholar
[14] Fehri, A., Velasco-Forero, S., Meyer, F.: Prior-based hierarchical segmentation highlighting structures of interest. In: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing. pp. 146–158. Springer (2017)10.1007/978-3-319-57240-6_12Search in Google Scholar
[15] Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in computer vision, pp. 726–740. Elsevier (1987)10.1016/B978-0-08-051581-6.50070-2Search in Google Scholar
[16] Gueguen, L., Velasco-Forero, S., Soille, P.: Local mutual information for dissimilarity-based image segmentation. JMIV pp. 1–20 (2013)10.1007/s10851-013-0432-9Search in Google Scholar
[17] Guigues, L., Cocquerez, J.P., Le Men, H.: Scale-sets image analysis. IJCV 68(3), 289–317 (2006)10.1007/s11263-005-6299-0Search in Google Scholar
[18] Jarník, V.: O jistém problému minimálním.(z dopisu panu o. borůvkovi)Search in Google Scholar
[19] Kiran, B.R., Serra, J.: Ground truth energies for hierarchies of segmentations. In: ISMM, pp. 123–134 (2013)10.1007/978-3-642-38294-9_11Search in Google Scholar
[20] Kiran, B.R., Serra, J.: Global-local optimizations by hierarchical cuts and climbing energies. Pattern Recognition 47(1), 12–24 (2014)10.1016/j.patcog.2013.05.012Search in Google Scholar
[21] Liu, Z., Zou, W., Li, L., Shen, L., Le Meur, O.: Co-saliency detection based on hierarchical segmentation. IEEE Signal Processing Letters 21(1), 88–92 (2014)10.1109/LSP.2013.2292873Search in Google Scholar
[22] Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3431–3440 (2015)10.1109/CVPR.2015.7298965Search in Google Scholar
[23] Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)10.1023/B:VISI.0000029664.99615.94Search in Google Scholar
[24] Machairas, V., Faessel, M., Cárdenas-Peña, D., Chabardes, T., Walter, T., Decencière, E.: Waterpixels. IEEE TIP 24(11), 3707–3716 (2015)10.1109/TIP.2015.2451011Search in Google Scholar PubMed
[25] Malmberg, F., Hendriks, C.L.L., Strand, R.: Exact evaluation of targeted stochastic watershed cuts. Discrete Applied Mathematics 216, 449–460 (2017)10.1016/j.dam.2016.01.006Search in Google Scholar
[26] Meyer, F.: Stochastic watershed hierarchies. In: ICAPR. pp. 1–8 (2015)10.1109/ICAPR.2015.7050646Search in Google Scholar
[27] Meyer, F., Beucher, S.: Morphological segmentation. Journal of visual communication and image representation 1(1), 21–46 (1990)10.1016/1047-3203(90)90014-MSearch in Google Scholar
[28] Meyer, F.: Minimum spanning forests for morphological segmentation. In: Mathematical morphology and its applications to image processing, pp. 77–84. Springer (1994)10.1007/978-94-011-1040-2_11Search in Google Scholar
[29] Najman, L., Cousty, J., Perret, B.: Playing with kruskal: algorithms for morphological trees in edge-weighted graphs. In: Mathematical Morphology and Its Applications to Signal and Image Processing, pp. 135–146. Springer (2013)10.1007/978-3-642-38294-9_12Search in Google Scholar
[30] Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Is object localization for free?-weakly-supervised learning with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 685–694 (2015)10.1109/CVPR.2015.7298668Search in Google Scholar
[31] Ouzounis, G.K., Pesaresi, M., Soille, P.: Differential area profiles: decomposition properties and eflcient computation. IEEE PAMI 34(8), 1533–1548 (2012)10.1109/TPAMI.2011.245Search in Google Scholar PubMed
[32] Perret, B., Cousty, J., Guimaraes, S.J.F., Maia, D.S.: Evaluation of hierarchical watersheds. IEEE Transactions on Image Processing 27(4), 1676–1688 (2018)10.1109/TIP.2017.2779604Search in Google Scholar PubMed
[33] Pont-Tuset, J., Arbelaez, P., Barron, J.T., Marques, F., Malik, J.: Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE transactions on pattern analysis and machine intelligence 39(1), 128–140 (2016)10.1109/TPAMI.2016.2537320Search in Google Scholar PubMed
[34] Prim, R.C.: Shortest connection networks and some generalizations. The Bell System Technical Journal 36(6), 1389–1401 (1957)10.1002/j.1538-7305.1957.tb01515.xSearch in Google Scholar
[35] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 779–788 (2016)10.1109/CVPR.2016.91Search in Google Scholar
[36] Ren, Z., Shakhnarovich, G.: Image segmentation by cascaded region agglomeration. In: CVPR. pp. 2011–2018 (2013)10.1109/CVPR.2013.262Search in Google Scholar
[37] Ronse, C.: Ordering partial partitions for image segmentation and filtering: Merging, creating and inflating blocks. JMIV pp. 1–32 (2013)10.1007/s10851-013-0455-2Search in Google Scholar
[38] Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: European conference on computer vision. pp. 430–443. Springer (2006)10.1007/11744023_34Search in Google Scholar
[39] Serra, J.: Tutorial on connective morphology. Selected Topics in Signal Processing, IEEE Journal of 6(7), 739–752 (Nov 2012)10.1109/JSTSP.2012.2220120Search in Google Scholar
[40] Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556 (2014)Search in Google Scholar
[41] Su, B., Lu, S., Tan, C.L.: Blurred image region detection and classification. In: ACM International Conference on Multimedia. pp. 1397–1400. MM ’11 (2011)10.1145/2072298.2072024Search in Google Scholar
[42] Vachier, C., Meyer, F.: Extinction value: a new measurement of persistence. In: IEEE Workshop on nonlinear signal and image processing. vol. 1, pp. 254–257 (1995)Search in Google Scholar
[43] Vargas-Muñoz, J.E., Chowdhury, A.S., Alexandre, E.B., Galvão, F.L., Miranda, P.A.V., Falcão, A.X.: An iterative spanning forest framework for superpixel segmentation. IEEE Transactions on Image Processing 28(7), 3477–3489 (2019)10.1109/TIP.2019.2897941Search in Google Scholar PubMed
[44] Veksler, O.: Star shape prior for graph-cut image segmentation. In: ECCV, pp. 454–467. Springer (2008)10.1007/978-3-540-88690-7_34Search in Google Scholar
[45] Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR. pp. 511–518 (2001)Search in Google Scholar
[46] Wei, X., Yang, Q., Gong, Y., Ahuja, N., Yang, M.H.: Superpixel hierarchy. IEEE Transactions on Image Processing 27(10), 4838–4849 (2018)10.1109/TIP.2018.2836300Search in Google Scholar PubMed
[47] Xu, Y., Géraud, T., Najman, L.: Hierarchical image simplification and segmentation based on mumford-shah-salient level line selection. Pattern Recognition Letters (2016)10.1016/j.patrec.2016.05.006Search in Google Scholar
[48] Zheng, L., Yang, Y., Tian, Q.: Sift meets cnn: A decade survey of instance retrieval. IEEE transactions on pattern analysis and machine intelligence 40(5), 1224–1244 (2017)10.1109/TPAMI.2017.2709749Search in Google Scholar PubMed
© 2019 Amin Fehri et al., published by De Gruyter Open
This work is licensed under the Creative Commons Attribution 4.0 Public License.