Abstract
Visual context provides cues about an object’s presence, position and size within an observed scene, which are used to increase the performance of object detection techniques. However, state-of-the-art methods for context aware object detection could decrease the initial performance. We discuss the reasons for failure and propose a concept that overcomes these limitations, by introducing a novel technique for integrating visual context and object detection. Therefore, we apply the prior probability function of an object detector, that maps the detector’s output to probabilities. Together, with an appropriate contextual weighting, a probabilistic framework is established. In addition, we present an extension to state-of-the-art methods to learn scale-dependent visual context information and show how this increases the initial performance. The standard methods and our proposed extensions are compared on a novel, demanding image data set. Results show that visual context facilitates object detection methods.
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References
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. Conf. Comp. Vis. Pattern Recog. (December 2001)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. Conf. Comp. Vis. Pattern Recog., vol. 2, pp. 886–893 (June 2005)
Palmer, S.E.: The effects of contextual scenes on the identification of objects. Mem. Cognit. 3, 519–526 (1975)
Biederman, I.: Perceptual Organization. In: On the semantics of a glance at a scene, pp. 213–263. Lawrence Erlbaum, Mahwah (1981)
Bar, M.: Visual objects in context. Nat. Rev. Neurosci. 5, 617–629 (2004)
Aminoff, E., Gronau, N., Bar, M.: The parahippocampal cortex mediates spatial and nonspatial associations. Cereb. Cortex 17(7), 1493–1503 (2007)
Torralba, A., Oliva, A., Castelhano, M.S., Henderson, J.M.: Contextual guidance of attention in natural scenes: The role of global features on object search. Psychol. Rev. 113(4), 766–786 (2006)
Hoiem, D., Efros, A.A., Hebert, M.: Putting objects in perspective. In: Proc. Conf. Comp. Vis. Pattern Recog., vol. 2, pp. 2137–2144 (June 2006)
Perko, R., Leonardis, A.: Context driven focus of attention for object detection. In: Paletta, L., Rome, E. (eds.) WAPCV 2007. LNCS, vol. 4840, pp. 216–233. Springer, Heidelberg (2007)
Torralba, A.: Contextual priming for object detection. Int. J. Comput. Vision 53(2), 153–167 (2003)
Bileschi, S.M.: StreetScenes: Towards Scene Understanding in Still Images. PhD thesis, Massachusetts Institute of Technology (May 2006)
Oliva, A., Torralba, A.: The role of context in object recognition. Trends in Cognit. Sci. 11(12), 520–527 (2007)
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: A database and web-based tool for image annotation. Technical Report AIM-2005-025, MIT AI Lab Memo (September 2005)
Wolf, L., Bileschi, S.M.: A critical view of context. Int. J. Comput. Vision 69(2), 251–261 (2006)
Itti, L., Koch, C.: Computational modeling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001)
Rasolzadeh, B., Targhi, A.T., Eklundh, J.O.: An Attentional System Combining Top-Down and Bottom-Up Influences. In: Paletta, L., Rome, E. (eds.) WAPCV 2007. LNCS, vol. 4840, pp. 123–140. Springer, Heidelberg (2007)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. J. Comput. Vision 42(3), 145–175 (2001)
Torralba, A., Sinha, P.: Statistical context priming for object detection. In: Proc. Int. Conf. Computer Vision, vol. 1, pp. 763–770 (July 2001)
Torralba, A.: Contextual modulation of target saliency. In: Neural Inf. Proc. Systems, vol. 14, pp. 1303–1310 (2002)
Hoiem, D., Efros, A.A., Hebert, M.: Geometric context from a single image. In: Proc. Int. Conf. Computer Vision, vol. 1, pp. 654–661 (October 2005)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Patter. Anal. Mach. Intell. 24(8), 1026–1038 (2002)
Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers 10(3), 61–74 (1999)
Seemann, E., Leibe, B., Schiele, B.: Multi-aspect detection of articulated objects. In: Proc. Conf. Comp. Vis. Pattern Recog., vol. 2, pp. 1582–1588 (June 2006)
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Perko, R., Wojek, C., Schiele, B., Leonardis, A. (2009). Integrating Visual Context and Object Detection within a Probabilistic Framework. In: Paletta, L., Tsotsos, J.K. (eds) Attention in Cognitive Systems. WAPCV 2008. Lecture Notes in Computer Science(), vol 5395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00582-4_5
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DOI: https://doi.org/10.1007/978-3-642-00582-4_5
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