Abstract
Robust category-level object recognition is currently a major goal for the Computer Vision community. Intra-class and pose variations, as well as, background clutter and partial occlusions are some of the main difficulties to achieve this goal. Contextual information in the form of object co-ocurrences and spatial contraints has been successfully applied to reduce the inherent uncertainty of the visual world. Recently, Choi et al. [5] propose the use of a tree-structured graphical model to capture contextual relations among objects. Under this model there is only one possible fixed contextual relation among subsets of objects. In this work we extent Choi et al. approach by using a mixture model to consider the case that contextual relations among objects depend on scene type. Our experiments highlight the advantages of our proposal, showing that the adaptive specialization of contextual relations improves object recognition and object detection performances.
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Keywords
- Object Recognition
- Object Detection
- Conditional Random Field
- Contextual Relation
- Computer Vision Community
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
Murphy, K., Torralba, A., Freeman, W.T.: The mit-csail database of objects and scenes (2010), http://web.mit.edu/torralba/www/database.html
Bartlett, M.S., Littlewort, G., Fasel, I., Chenu, J., Ishiguro, H., Movellan, J.R.: Towards social robots: Automatic evaluation of human-robot interaction by face detection and expression classification. In: Advances in Neural Information. MIT Press (2003)
Chang, E., Goh, K., Sychay, G., Wu, G.: Cbsa: Content-based soft annotation for multimodal image retrieval using bayes point machines. IEEE Transactions on Circuits and Systems for Video Technology 13, 26–38 (2003)
Choi, M.: Large database of object categories (2010), http://web.mit.edu/~myungjin/www/HContext.html
Choi, M.J., Lim, J.J., Torralba, A., Willsky, A.S.: Exploiting hierarchical context on a large database of object categories. In: IEEE Conference on Computer VIsion and Pattern Recognition, CVPR (2010)
Chow, C.I., Liu, C.N.: Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory 14, 462–467 (1968)
Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: ICML 2006: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240. ACM Press (2006)
Ekvall, S., Kragic, D., Jensfelt, P.: Object detection and mapping for service robot tasks. Robotica 25(2), 175–187 (2007)
Espinace, P., Kollar, T., Soto, A., Roy, N.: Indoor scene recognition through object detection. In: Proc. of IEEE Int. Conf. on Robotics and Automation, ICRA (2010)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) challenge. International Journal of Computer Vision 88(2), 303–338 (2010)
Fei-Fei, L.: A bayesian hierarchical model for learning natural scene categories. In: CVPR, vol. 2, pp. 524–531 (2005)
Felzenszwalb, P.F., McAllester, D.A., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2008)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: CVPR, pp. 264–271 (2003)
Galleguillos, C., Belongie, S.: Context based object categorization: A critical survey. Computer Vision and Image Understanding 114(6), 712–722 (2010); special Issue on Multi-Camera and Multi-Modal Sensor Fusion
Huebner, K., Björkman, M., Rasolzadeh, B., Schmidt, M., Kragic, D.: Integration of Visual and Shape Attributes for Object Action Complexes. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 13–22. Springer, Heidelberg (2008)
Lowe, D.: Object recognition from local scale-invariant features, pp. 1150–1157 (1999)
Pearl, J.: Reverend Bayes on inference engines: A distributed hierarchical approach. In: Proceedings of the American Association of Artificial Intelligence National Conference on AI, Pittsburgh, PA, pp. 133–136 (1982)
Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., Belongie, S.: Objects in context. In: ICCV 2007, pp. 1–8 (2007)
Shotton, J., Winn, J., Rother, C., Criminisi, A.: Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context (2007)
Sinha, P., Torralba, A.: Detecting faces in impoverished images. Journal of Vision 2(7), 601 (2002)
Torralba, A., Murphy, K.P., Freeman, W.T.: Contextual models for object detection using boosted random fields. In: Advances in Neural Information Processing Systems 17 (NIPS), pp. 1401–1408 (2005)
Torralba, A.: Contextual priming for object detection. IJCV 53 (2003)
Ulrich, I., Nourbakhsh, I.: Appearance-based place recognition for topological localization. In: Proceedings of IEEE International Conference on Robotics and Automation, ICRA 2000, vol. 2, pp. 1023–1029 (2000)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features, pp. 511–518 (2001)
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Peralta, B., Soto, A. (2011). Mixing Hierarchical Contexts for Object Recognition. In: San Martin, C., Kim, SW. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2011. Lecture Notes in Computer Science, vol 7042. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25085-9_27
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