Abstract
Current approaches to visual object class detection mainly focus on the recognition of basic level categories, such as cars, motorbikes, mugs and bottles. Although these approaches have demonstrated impressive performance in terms of recognition, their restriction to these categories seems inadequate in the context of embodied, cognitive agents. Here, distinguishing objects according to functional aspects based on object affordances is important in order to enable manipulation of and interaction between physical objects and cognitive agent.
In this paper, we propose a system for the detection of functional object classes, based on a representation of visually distinct hints on object affordances (affordance cues). It spans the complete range from tutor-driven acquisition of affordance cues, learning of corresponding object models, and detecting novel instances of functional object classes in real images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bogoni, L., Bajcsy, R.: Interactive recognition and representation of functionality. In: CVIU, vol. 62(2), pp. 194–214 (1995)
Csurka, G., Dance, C.R., Fan, L., Willarnowski, J., Bray, C.: Visual categorization with bags of keypoints. In: SLCV (2004)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results.
Ferrari, V., Tuytelaars, T., Van Gool, L.J.: Object detection by contour segment networks. In: ECCV (2006)
Ferrari, V., Fevrier, L., Jurie, F., Schmid, C.: Groups of adjacent contour segments for object detection. Rapport De Recherche Inria (2006)
Gibson, J.J.: The theory of affordance. In: Percieving, Acting, and Knowing, Lawrence Erlbaum Associates, Hillsdale, NJ (1977)
Griffin, G., Holub, A., Perona, P.: Caltech-256 object category dataset. Technical Report 7694, California Institute of Technology (2007)
Jones, M.J., Rehg, J.M.: Statistical color models with application to skin detection. In: CVPR, pp. 1274–1280. IEEE Computer Society, Los Alamitos (1999)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML 2001 (2001)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR 2006 (2006)
Leibe, B., Leonardis, A., Schiele, B.: An implicit shape model for combined object categorization and segmentation. In: Toward Category-Level Object Recognition, Springer, Heidelberg (2006)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.J.: A comparison of affine region detectors. In: IJCV 2005 (2005)
Rivlin, E., Dickinson, S.J., Rosenfeld, A.: Recognition by functional parts. Computer Vision and Image Understanding: CVIU 62(2), 164–176 (1995)
Rosch, E., Mervis, C.B., Gray, W.D., Johnson, D.M., Braem, P.B.: Basic objects in natural categories. Cognitive Psychology (1976)
Saxena, A., Driemeyer, J., Ng, A.Y.: Robotic grasping of novel objects using vision. In: IJRR (2007)
Stark, L., Bowyer, K.: Achieving generalized object recognition through reasoning about association of function to structure. PAMI 13(10), 1097–1104 (1991)
Stark, L., Hoover, A.W., Goldgof, D.B., Bowyer, K.W.: Function-based recognition from incomplete knowledge of shape. In: WQV 1993, pp. 11–22 (1993)
Stark, M., Schiele, B.: How good are local features for classes of geometric objects. In: ICCV (October 2007)
Sun, J., Zhang, W.W., Tang, X., Shum, H.Y.: Background cut. In: ECCV II, pp. 628–641 (2006)
Winston, P.H., Katz, B., Binford, T.O., Lowry, M.R.: Learning physical descriptions from functional definitions, examples, and precedents. In: AAAI 1983 (1983)
Zillich, M.: Incremental Indexing for Parameter-Free Perceptual Grouping. In: 31st Workshop of the Austrian Association for Pattern Recognition (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Stark, M., Lies, P., Zillich, M., Wyatt, J., Schiele, B. (2008). Functional Object Class Detection Based on Learned Affordance Cues. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds) Computer Vision Systems. ICVS 2008. Lecture Notes in Computer Science, vol 5008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79547-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-540-79547-6_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79546-9
Online ISBN: 978-3-540-79547-6
eBook Packages: Computer ScienceComputer Science (R0)