Abstract
The chapter describes visual classification by a hierarchy of semantic fragments. In fragment-based classification, objects within a class are represented by common sub-structures selected during training. The chapter describes two extensions to the basic fragment-based scheme. The first extension is the extraction and use of feature hierarchies. We describe a method that automatically constructs complete feature hierarchies from image examples, and show that features constructed hierarchically are significantly more informative and better for classification compared with similar non-hierarchical features. The second extension is the use of so-called semantic fragments to represent object parts. The goal of a semantic fragment is to represent the different possible appearances of a given object part. The visual appearance of such object parts can differ substantially, and therefore traditional image similarity-based methods are inappropriate for the task. We show how the method can automatically learn the part structure of a new domain, identify the main parts, and how their appearance changes across objects in the class. We discuss the implications of these extensions to object classification and recognition.
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References
Agarwal, S., Awan, A., Roth, D.: Learning to detect objects in images via a sparse, part-based representation. IEEE TPAMI 26(11), 1475–1490 (2004)
Bart, E., Ullman, S.: Class-based matching of object parts. In: Proc. CVPR Workshop on Image and Video Registration (2004)
Biederman, I.: Recognition-by-Components: A Theory of Human Image Understanding. Psychological Review 94(2), 115–147 (1987)
Epshtein, B., Ullman, S.: Identifying Semantically Equivalent Object Fragments. In: CVPR, pp. 2–9 (2005)
Epshtein, B., Ullman, S.: Feature Hierarchies for Object Classification. In: ICCV (to appear, 2005)
Fergus, R., Perona, P., Zisserman, A.: Object Class Recognition by Unsupervised Scale-Invariant Learning. In: CVPR, pp. 264–271 (2003)
Foldiak, P.: Learning invariance from transformation sequences. Neural Computation 3(2), 194–200 (1991)
Green, D., Swets, J.: Signal Detection Theory and Psychophysics. Wiley, NY (1966)
Heisele, B., Serre, T., Pontil, M., Vetter, T., Poggio, T.: Categorization by learning and combining object parts. In: NIPS (2001)
Itti, L., Kosh, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE TPAMI 20(11), 1254–1259 (1998)
LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., Jackel, L.: Backpropagation applied to handwritten zip code recognition. Neural Computation 1(4), 541–551 (1989)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comp. Vis. 60(2), 91–100 (2004)
Marr, D., Nishihara, H.: Representation and recognition of the spatial organization of three dimensional structure. Proceedings of the Royal Society of London B 200, 269–294 (1978)
Mikolajczyk, K., Schmidt, C.: A performance evaluation of local descriptors. In: CVPR, pp. 257–264 (2003)
Mikolajczyk, K., Schmidt, C.: Scale and affine invariant point detectors. Int. J. Comp. Vis. 60(1), 63–86 (2004)
Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience 2(11), 1019–1025 (1999)
Stringer, S., Rolls, E.: Invariant object recognition in the visual system with novel view of 3D objects. Neural Computation 14, 2585–2596 (2002)
Tomasi, C., Kanade, T.: Detecting and tracking of point features. Technical Report CMU-CS-91-132, Carnegie Mellon University (1991)
Ullman, S., Bart, E.: Recognition invariance obtained by extended and invariant features. Neural Networks 17, 833–848 (2004)
Ullman, S., Soloviev, S.: Computation of pattern invariance in brain-like structures. Neural Networks 12, 1021–1036 (1999)
Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nature Neuroscience 5(7), 1–6 (2002)
Vidal-Naquet, M., Ullman, S.: Object Recognition with Informative Features and Linear Classification. In: ICCV, pp. 281–288 (2003)
Wiskott, L., Fellous, J., Kruger, N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE TPAMI 19(7), 775–779 (1997)
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Ullman, S., Epshtein, B. (2006). Visual Classification by a Hierarchy of Extended Fragments. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds) Toward Category-Level Object Recognition. Lecture Notes in Computer Science, vol 4170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11957959_17
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DOI: https://doi.org/10.1007/11957959_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68794-8
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