Abstract
Most object detection techniques discussed in the literature are based solely on texture-based features that capture the global or local appearance of an object. While results indicate their ability to effectively represent an object class, these features can be detected repeatably only in the object interior, and so cannot effectively exploit the powerful recognition cue of contour. Since generic object classes can be characterized by shape and appearance, this paper has formulated a method to combine these attributes to enhance the object model. We present an approach for incorporating the recently introduced shape-based features called k-Adjacent-Segments (kAS) in our appearance-based framework based on dense SIFT features. Class-specific kAS features are detected in an arbitrary image to form a shape map that is then employed in two novel ways to augment the appearance-based technique. This is shown to improve the detection performance for all classes in the challenging 3D dataset by 3-18% and the PASCAL VOC 2006 by 5%.
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References
Vidal-Naquet, M., Ullman, S.: Object Recognition with Informative Features and Linear Classification. In: ICCV, pp. 281–288 (2003)
Gill, G., Levine, M.: Multi-view Object Detection using Spatial Consistency in a Low Dimensional Space. Accepted in DAGM (September 2009)
Dorkó, G., Schmid, C.: Selection of Scale-Invariant Parts for Object Class Recognition. In: ICCV, pp. 634–640 (2003)
Savarese, S., Fei-Fei, L.: 3D Generic Object Categorization, Localization and Pose Estimation. In: ICCV, October 2007, pp. 1–8 (2007)
Shotton, J., Blake, A., Cipolla, R.: Contour-Based Learning for Object Detection. In: ICCV, vol. 1, pp. 503–510 (2005)
Opelt, A., Pinz, A., Zisserman, A.: A Boundary-Fragment-Model for Object Detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 575–588. Springer, Heidelberg (2006)
Jurie, F., Schmid, C.: Scale-Invariant Shape Features for Recognition of Object Categories. In: CVPR, vol. II, pp. 90–96 (2004)
Ferrari, V., Fevrier, L., Jurie, F., Schmid, C.: Groups of Adjacent Contour Segments for Object Detection. IEEE Transactions PAMI 30(1), 36–51 (2008)
Zhang, W., Yu, B., Zelinsky, G., Samaras, D.: Object Class Recognition using Multiple Layer Boosting with Multiple Features. In: CVPR, pp. II:323–II:330 (2005)
Opelt, A., Zisserman, A., Pinz, A.: Fusing Shape and Appearance Information for Object Category Detection. In: BMVC, vol. 1, pp. 117–126 (2006)
Shotton, J., Blake, A., Cipolla, R.: Efficiently Combining Contour and Texture Cues for Object Recognition. In: BMVC (2008)
Lowe, D.G.: Object Recognition from Local Scale-Invariant Features. In: ICCV, vol. 2, pp. 1150–1157 (1999)
Everingham, M., Zisserman, A., Williams, C.K.I., Van Gool, L.: The PASCAL Visual Object Classes Challenge (VOC 2006) Results (2006), http://www.pascal-network.org/challenges/VOC/voc2006/results.pdf
de Ridder, D., Duin, R.: Locally Linear Embedding for Classification. Technical Report PH-2002-01, Pattern Recognition Group, Delft Univ. of Tech., Delft (2002)
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Gill, G., Levine, M. (2009). Incorporating Shape Features in an Appearance-Based Object Detection System. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_33
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DOI: https://doi.org/10.1007/978-3-642-03767-2_33
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