Abstract
The importance of spatial configuration information for object class recognition is widely recognized. Single isolated local appearance codes are often ambiguous. On the other hand, object classes are often characterized by groups of local features appearing in a specific spatial structure. Learning these structures can provide additional discriminant cues and boost recognition performance. However, the problem of learning such features automatically from raw images remains largely uninvestigated. In contrast to previous approaches which require accurate localization and segmentation of objects to learn spatial information, we propose learning by hierarchical voting to identify frequently occurring spatial relationships among local features directly from raw images. The method is resistant to common geometric perturbations in both the training and test data. We describe a novel representation developed to this end and present experimental results that validate its efficacy by demonstrating the improvement in class recognition results realized by including the additional learned information.
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References
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 264–271 (2003)
Leibe, B., Mikolajczyk, K., Schiele, B.: Efficient clustering and matching for object class recognition. In: British Machine Vision Conference, Edinburgh, England (2006)
Berg, A.C., Berg, T.L., Malik, J.: Shape matching and object recognition using low distortion correspondences. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 26–33. IEEE Computer Society Press, Los Alamitos (2005)
Dorko, G., Schmid, C.: Object class recognition using discriminative local features (2005)
Ortega, M., Rui, Y., Chakrabarti, K., Mehrotra, S., Huang, T.S.: Supporting similarity queries in mars. In: ACM International Conference on Multimedia, pp. 403–413. ACM Press, New York (1997)
Carson, C., Thomas, M., Belongie, S., Hellerstein, J., Malik, J.: Blobworld: a system for region-based image indexing and retrieval. Technical report, Berkeley, CA, USA (1999)
Mukherjea, S., Hirata, K., Hara, Y.: Amore: a world-wide web image retrieval engine. In: CHI 1999. Extended abstracts on human factors in computing systems, pp. 17–18. ACM Press, New York (1999)
Malik, J., Belongie, S., Shi, J., Leung, T.K.: Textons, contours and regions: Cue integration in image segmentation. In: IEEE International Conference on Computer Vision, pp. 918–925. IEEE Computer Society Press, Los Alamitos (1999)
Lowe, D.G.: Object recognition from local scale-invariant features. In: IEEE International Conference on Computer Vision, vol. 1150, IEEE Computer Society Press, Los Alamitos (1999)
Lazebnik, S., Schmid, C., Ponce, J.: Affine-invariant local descriptors and neighborhood statistics for texture recognition. In: IEEE International Conference on Computer Vision, vol. 649, IEEE Computer Society, Los Alamitos (2003)
Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1470–1477 (2003)
Lipson, P., Grimson, E., Sinha, P.: Configuration based scene classification and image indexing. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1007, IEEE Computer Society, Los Alamitos (1997)
Zhang, W., Yu, B., Zelinsky, G.J., Samaras, D.: Object class recognition using multiple layer boosting with heterogeneous features. In: IEEE Conference on Computer Vision and Pattern Recognition
Amit, Y., Geman, D.: A computational model for visual selection. Neural Comput. 11, 1691–1715 (1999)
Agarwal, A., Triggs, W.: Hyperfeatures - multilevel local coding for visual recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, Springer, Heidelberg (2006)
Sinha, P.: Image invariants for object recognition. Invest. Opth. & Vis. Sci. 34(6) (1994)
Shokoufandeh, A., Dickinson, S.J., Jönsson, C., Bretzner, L., Lindeberg, T.: On the representation and matching of qualitative shape at multiple scales. In: European Conference on Computer Vision, pp. 759–775. Springer, Heidelberg (2002)
Fidler, S., Berginc, G., Leonardis, A.: Hierarchical statistical learning of generic parts of object structure. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 182–189. IEEE Computer Society Press, Los Alamitos (2006)
Witten, I.H., Moffat, A., Bell, T.C.: Managing gigabytes: compressing and indexing documents and images, 2nd edn. Morgan Kaufmann Publishers Inc, San Francisco (1999)
Everingham, M., Zisserman, A., Williams, C.K.I., Van Gool, L.: The PASCAL Visual Object Classes Challenge. In: VOC2006 (2006)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley, Reading (1999)
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Mekuz, N., Tsotsos, J.K. (2007). Hierarchical Learning of Dominant Constellations for Object Class Recognition. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_46
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DOI: https://doi.org/10.1007/978-3-540-76386-4_46
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