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Unsupervised colour image segmentation by low-level perceptual grouping

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Abstract

This paper proposes a new unsupervised approach for colour image segmentation. A hierarchy of image partitions is created on the basis of a function that merges spatially connected regions according to primary perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with five unsupervised colour image segmentation techniques. These techniques have been frequently used as a reference in other comparisons. The results obtained by each method have been systematically evaluated using four well-known unsupervised measures for judging the segmentation quality. Our methodology has globally shown the best performance, obtaining better results in three out of four of these segmentation quality measures. Experiments will also show that our proposal finds low-level perceptual solutions that are highly correlated with the ones provided by humans.

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Notes

  1. Note that each measure has different criteria and these criteria are particularly important for understanding the graphical results offered in Sect. 3.3.

  2. The same scale for y-axes has been applied in both plots.

  3. Image results for GSEG algorithm were provided by the author for all the BSD and they offer an average time of 24 s per image in their paper. Likewise, our implementation and the ones for FH, MS and JSEG algorithms have been programmed in C language, however, the SRM algorithm were provided in MATLAB by the authors.

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Acknowledgements

This work was supported by the Spanish Ministry of Science and Innovation under the projects Consolider Ingenio 2010 CSD2007-00018, AYA2008-05965-C04-04/ESP and by Caixa-Castelló foundation under the project P1 1B2007-48. We would like to deeply thank Dr. Jason Fritts and Dr. Hui Zhang for their help towards implementing the unsupervised measures for evaluating the segmentation quality. We would also thank to Dr. Richard Nock, Dr. Sreenath Rao Vantaram and Dr. Pablo Arbelaez for their help detailing the SRM and GSEG algorithms and the Berkeley segmentation database respectively.

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  1. Department of Computer Languages and Systems, Institute of New Imaging Technologies, Universitat Jaume I, Av. Sos Baynat s/n, 12071, Castellón, Spain

    Adolfo Martínez-Usó, Filiberto Pla & Pedro García-Sevilla

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  1. Adolfo Martínez-Usó
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  2. Filiberto Pla
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  3. Pedro García-Sevilla
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Correspondence to Adolfo Martínez-Usó.

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Martínez-Usó, A., Pla, F. & García-Sevilla, P. Unsupervised colour image segmentation by low-level perceptual grouping. Pattern Anal Applic 16, 581–594 (2013). https://doi.org/10.1007/s10044-011-0259-1

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