Abstract
In this paper we explore the use of ranking as a mean of guiding unsupervised image segmentation. Starting by the well known Pagerank algorithm we introduce an extension based on quantum walks. Pagerank (rank) can be used to prioritize the merging of segments embedded in uniform regions (parts of the image with roughly similar appearance statistics). Quantum Pagerank, on the other hand, gives high priority to boundary segments. This latter effect is due to the higher order interactions captured by quantum fluctuations. However we found that qrank does not always outperform its classical version. We analyze the Pascal VOC database and give Intersection over Union (IoU) performances.
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References
Grady, L.: Random walks for image segmentation. TPAMI 28(11), 1768–1783 (2006)
Burges, C.J.C., Platt, J.C.: Semi-supervised learning with conditional harmonic mixing. In: Chapelle, O., Schölkopf, B., Zien, A. (eds.) Semi-Supervised Learning. MIT Press, Cambridge (2006)
Zhou, D., Huang, J., Schšlkopf, B.: Learning from labeled and unlabeled data on a directed graph. In: ICML, pp. 1041–1048 (2005)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. TPAMI 34(11), 2274–2282 (2012)
Nock, R., Nielsen, F.: Statistical region merging. TPAMI 26(11), 1452–1458 (2004)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web (1999)
Johns, J., Mahadevan, S.: Constructing basis functions from directed graphs for value function approximation. In: ICML, pp. 385–392 (2007)
Langville, A.N., Meyer, C.D.: Deeper inside pagerank. Internet Mathematics 1, 335–400 (2004)
Aharonov, Y., Davidovich, L., Zagury, N.: Quantum random walks. Phys. Rev. A 48, 1687–1690 (1993)
Grover, L.K.: A fast quantum mechanical algorithm for database search. In: ACM Symposium on Theory of Computing, pp. 212–219 (1996)
Szegedy, M.: Quantum speed-up of markov chain based algorithms. In: FOCS, pp. 32–41. IEEE Computer Society (2004)
Paparo, G.D., Martin-Delgado, M.A.: Google in a quantum network. CoRR abs/1112.2079 (2011)
Paparo, G.D., M’́uller, M., Comellas, F., Martin-Delgado, M.A.: Quantum google in a complex network. Scientific Reports 3, 2773 (2013)
Carreira, J., Sminchisescu, C.: Cpmc: Automatic object segmentation using constrained parametric min-cuts. TPAMI 34(7), 1312–1328 (2012)
Maire, M., Arbelaez, P., Fowlkes, C.C., Malik, J.: Using contours to detect and localize junctions in natural images. In: CVPR (2008)
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Escolano, F., Bonev, B., Hancock, E.R. (2014). Quantum vs Classical Ranking in Segment Grouping. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2014. Lecture Notes in Computer Science, vol 8621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44415-3_21
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DOI: https://doi.org/10.1007/978-3-662-44415-3_21
Publisher Name: Springer, Berlin, Heidelberg
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