Abstract
Region proposal methods provide richer object hypotheses than sliding windows with dramatically fewer proposals, yet they still number in the thousands. This large quantity of proposals typically results from a diversification step that propagates bottom-up ambiguity in the form of proposals to the next processing stage. In this paper, we take a complementary approach in which mid-level knowledge is used to resolve bottom-up ambiguity at an earlier stage to allow a further reduction in the number of proposals. We present a method for generating regions using the mid-level grouping cues of closure and symmetry. In doing so, we combine mid-level cues that are typically used only in isolation, and leverage them to produce fewer but higher quality proposals. We emphasize that our model is mid-level by learning it on a limited number of objects while applying it to different objects, thus demonstrating that it is transferable to other objects. In our quantitative evaluation, we (1) establish the usefulness of each grouping cue by demonstrating incremental improvement, and (2) demonstrate improvement on two leading region proposal methods with a limited budget of proposals.
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References
Carreira, J., Sminchisescu, C.: Cpmc: Automatic object segmentation using constrained parametric min-cuts. PAMI 34, 1312–1328 (2012)
Uijlings, J., van de Sande, K., Gevers, T., Smeulders, A.: Selective search for object recognition. IJCV 104, 154–171 (2013)
Fidler, S., Mottaghi, R., Yuille, A., Urtasun, R.: Bottom-up segmentation for top-down detection. In: CVPR, pp. 3294–3301 (2013)
Fidler, S., Boben, M., Leonardis, A.: Learning a hierarchical compositional shape vocabulary for multi-class object representation. ArXiv:1408.5516 (2014)
Elder, J., Zucker, S.: Computing contour closure. In: Buxton, B.F., Cipolla, R. (eds.) ECCV 1996. LNCS, vol. 1064, pp. 399–412. Springer, Heidelberg (1996)
Jacobs, D.: Robust and efficient detection of convex groups. PAMI 18(1), 23–37 (1996)
Loy, G., Eklundh, J.-O.: Detecting symmetry and symmetric constellations of features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 508–521. Springer, Heidelberg (2006)
Mohan, R., Nevatia, R.: Perceptual organization for scene segmentation and description. PAMI 14, 616–635 (1992)
Tsogkas, S., Kokkinos, I.: Learning-based symmetry detection in natural images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 41–54. Springer, Heidelberg (2012)
Blum, H.: A transformation for extracting new descriptors of shape. In: Wathen-Dunn, W. (ed.) Models for the Perception of Speech and Visual Form, pp. 362–380. MIT Press, Cambridge (1967)
Binford, T.: Visual perception by computer. In: ICSC (1971)
Pentland, A.: Perceptual organization and the representation of natural form. AI 28, 293–331 (1986)
Biederman, I.: Human image understanding: Recent research and a theory. In: CVGIP (1985)
Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 109–122. Springer, Heidelberg (2002)
Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: CVPR (2007)
Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR, pp. 73–80 (2010)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. IJCV 59, 167–181 (2004)
Arbeláez, P., Pont-Tuset, J., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping. In: CVPR (2014)
Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. PAMI 33, 898–916 (2011)
Kim, J., Grauman, K.: Boundary preserving dense local regions. In: CVPR (2011)
Endres, I., Hoiem, D.: Category independent object proposals. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 575–588. Springer, Heidelberg (2010)
Zhu, S., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. PAMI 18, 884–900 (1996)
Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22, 888–905 (2000)
Leung, T., Malik, J.: Contour continuity in region based image segmentation. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 544–559. Springer, Heidelberg (1998)
Ren, X., Fowlkes, C., Malik, J.: Cue integration for figure/ground labeling. In: NIPS (2005)
Levinshtein, A., Sminchisescu, C., Dickinson, S.J.: Optimal image and video closure by superpixel grouping. IJCV 100, 99–119 (2012)
Lee, T., Fidler, S., Dickinson, S.: Detecting curved symmetric parts using a deformable disc model. In: ICCV (2013)
Kolmogorov, V., Boykov, Y., Rother, C.: Applications of parametric maxflow in computer vision. In: ICCV, vol. 8 (2007)
Schwing, A., Fidler, S., Pollefeys, M., Urtasun, R.: Box in the box: Joint 3d layout and object reasoning from single images. In: ICCV (2013)
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Lee, T., Fidler, S., Dickinson, S. (2015). Multi-cue Mid-level Grouping. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_25
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