Abstract
We propose a category-independent method to produce a bag of regions and rank them, such that top-ranked regions are likely to be good segmentations of different objects. Our key objectives are completeness and diversity: every object should have at least one good proposed region, and a diverse set should be top-ranked. Our approach is to generate a set of segmentations by performing graph cuts based on a seed region and a learned affinity function. Then, the regions are ranked using structured learning based on various cues. Our experiments on BSDS and PASCAL VOC 2008 demonstrate our ability to find most objects within a small bag of proposed regions.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: CVPR (2009)
Lampert, C.H., Nickisch, H., Harmeling, S.: Learning to detect unseen object classes by between-class attribute transfer. In: CVPR (2009)
Goodale, M.A., Milner, A.D., Jakobson, L.S., Carey, D.P.: A neurological dissociation between perceiving objects and grasping them. Nature 349, 154–156 (2000)
Hoiem, D., Stein, A.N., Efros, A.A., Hebert, M.: Recovering occlusion boundaries from an image. In: ICCV (2007)
Martin, D., Fowlkes, C., Malik, J.: Learning to find brightness and texture boundaries in natural images. In: NIPS (2002)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge, VOC 2008 Results (2008), http://www.pascal-network.org/challenges/VOC/voc2008/workshop/index.html
Viola, P., Jones, M.J.: Robust real-time face detection. IJCV 57 (2004)
Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: CVPR (2008)
Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. IJCV 77, 259–289 (2008)
Maji, S., Malik, J.: Object detection using a max-margin hough transform. In: CVPR, pp. 1038–1045. IEEE Computer Society, Los Alamitos (2009)
Chum, O., Zisserman, A.: An exemplar model for learning object classes. In: CVPR (2007)
Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple kernels for object detection. In: ICCV (2009)
Gu, C., Lim, J., Arbelaez, P., Malik, J.: Recognition using regions. In: CVPR, pp. 1030–1037 (2009)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. PAMI 22 (2000)
Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. IJCV 59 (2004)
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: From contours to regions: An empirical evaluation. In: CVPR, pp. 2294–2301 (2009)
Sharon, E., Galun, M., Sharon, D., Basri, R., Brandt, A.: Hierarchy and adaptivity in segmenting visual cues. Nature (2006)
Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In ICCV (2005)
Malisiewicz, T., Efros, A.A.: Improving spatial support for objects via multiple segmentations. In: BMVC (2007)
Russell, B.C., Efros, A.A., Sivic, J., Freeman, W.T., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: CVPR (2006)
Stein, A., Stepleton, T., Hebert, M.: Towards unsupervised whole-object segmentation: Combining automated matting with boundary detection. In: CVPR (2008)
Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: ICCV (2009)
Walther, D., Koch, C.: 2006 special issue: Modeling attention to salient proto-objects. Neural Networks 19, 1395–1407 (2006)
Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.: Learning to detect a salient object. In: CVPR, pp. 1–8 (2007)
Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: CVPR (2010)
Carreira, J., Sminchisescu, C.: Constrained parametric min cuts for automatic object segmentation. In: CVPR (2010)
Rother, C., Kolmogorov, V., Blake, A.: “grabcut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23, 309–314 (2004)
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Technical report, MIT (2005)
Hoiem, D., Efros, A.A., Hebert, M.: Recovering surface layout from an image. IJCV 75, 151–172 (2007)
Szummer, M., Kohli, P., Hoiem, D.: Learning crfs using graph cuts. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 582–595. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Endres, I., Hoiem, D. (2010). Category Independent Object Proposals. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15555-0_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-15555-0_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15554-3
Online ISBN: 978-3-642-15555-0
eBook Packages: Computer ScienceComputer Science (R0)