Abstract
The task of visual classification is the recognition of an object in the image as belonging to a general class of similar objects, such as a face, a car, a dog, and the like. This is a fundamental and natural task for biological visual systems, but it has proven difficult to perform visual classification by artificial computer vision systems. The main reason for this difficulty is the variability of shape within a class: different objects vary widely in appearance, and it is difficult to capture the essential shape features that characterize the members of one category and distinguish them from another, such as dogs from cats.
In this paper we describe an approach to classification using a fragment-based representation. In this approach, objects within a class are represented in terms of common image fragments that are used as building blocks for representing a large variety of different objects that belong to a common class. The fragments are selected from a training set of images based on a criterion of maximizing the mutual information of the fragments and the class they represent. For the purpose of classification the fragments are also organized into types, where each type is a collection of alternative fragments, such as different hairline or eye regions for face classification. During classification, the algorithm detects fragments of the different types, and then combines the evidence for the detected fragments to reach a final decision. Experiments indicate that it is possible to trade off the complexity of fragments with the complexity of the combination and decision stage, and this tradeoff is discussed.
The method is different from previous part-based methods in using class-specific object fragments of varying complexity, the method of selecting fragments, and the organization into fragment types. Experimental results of detecting face and car views show that the fragment-based approach can generalize well to a variety of novel image views within a class while maintaining low mis-classification error rates. We briefly discuss relationships between the proposed method and properties of parts of the primate visual system involved in object perception.
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
Amit, Y., Geman, D., Wilder, K.: Joint Induction of Shape Features and Tree Classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, 11 (1997) 1300–1306
Bhat, D., Nayar, K. S.: Ordinal Measures for Image Correspondence. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 20, 4 (1998) 415–423
Biederman, I.: Human image understanding: recent research and theory. Computer Vision, Graphics and Image Processing 32 (1985) 29–73
Binford, T. O.: Visual perception by computer. IEEE conf. on systems and control, Vol. 94, 2 (1971) 115–147
Chow, C.K., Liu, C.N.: Approximating Discrete Probability Distributions with Dependence Trees. IEEE Transactions on Information Theory, Vol. 14, 3 (1968) 462–467
Cover, T.M. & Thomas, J.A.: Elements of Information Theory. Wiley Series in Telecommunication, New York (1991)
Edelman, S.: Representing 3D objects by sets of activities of receptive fields. Biological cybernitics 70 (1993) 37–45
Gallant, J.L., Braun, J., Van Essen, D.C.: Selectivity for polar, hyperbolic, and cartesian gratings in macaque visual cortex. Science, 259 (1993) 100–103
Green, D. M., Swets, J. A.: Signal Detection Theory and Psychophysics. Wiley, Chichester New York Brisbane Toronto (1966). Reprinted by Krieger, Huntingdon, New York (1974)
Hubel, D. H., Wiesel, T. N.: Receptive fields and functional architecture of monkey striate cortex. Journal of physiology 195 (1968) 215–243
Logothetis, N. K., Pauls J., Bülthoff H. H., Poggio T.: View-dependent object recognition in monkeys. Current biology, 4 (1994) 401–414
Marr, D.: Vision. W.H. Freeman, San Francisco (1982)
Marr, D., Nishihara, H. K.: Representation and recognition of the spatial organization of three dimensional structure. Proceedings of the Royal Society of London B, 200 (1978) 269–294
Mel, W. B.: SEEMORE: Combining color, shape and texture histogramming in a neurally inspired approach to visual object recognition. Neural computation 9 (1997) 777–804
Minsky, M., Papert, S.: Perceptrons. The MIT Press, Cambridge, Massachusetts (1969)
Miyashita, Y., Chang, H.S.: Neuronal correlate of pictorial short-term memory in the primate temporal cortex. Nature, 331, (1988) 68–70
Murase, H., Nayar, S.K.: Visual learning and recognition of 3-D objects from appearance. International J. of Com. Vision, 14 (1995) 5–24
Nelson, C. R., Selinger A.: A Cubist approach to object recognition. International Conference on Computer Vision '98 (1998) 614–621
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufman Publishers, San Mateo, California (1988)
Perret, D. I., Rolls, E. T., Caan W.: Visual neurons responsive to faces in the monkey temporal cortex. Experimental brain research, 47 (1982) 329–342
Poggio, T., Sung, K.: Finding human faces with a gaussian mixture distribution-base face model. Computer analysis of image and patterns (1995) 432–439
Poggio, T., Edelman, S.: A network that learns to recognize three-dimensional objects. Nature, 343 (1990) 263–266
Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience, Vol. 2, 11 (1999) 1019–1025
Rolls, E. T.: Neurons in the cortex of the temporal lobe and in the amygdala of the monkey with responses selective for faces. Human neurobiology, 3 (1984) 209–222
Rosch, E. Mervis, C.B., Gray, W.D., Johnson, S.M., Boyes-Braem, P.: Basic objects in natural categories. Cognitive Psychology, 8 (1976) 382–439
Schneiderman, H., Kanade. T.: Probabilistic modeling of local appearance and spatial relationships for object recognition. Proc. IEEE Comp. Soc. Conference on Computer Vision and Pattern Recognition, CVPR98, (1998) 45–51
Tanaka, K.: Neural Mechanisms of Object Recognition. Science, Vol. 262 (1993) 685–688.
Turk M., Pentland A.: "Eigenfaces for recognition", Cognitive Neuroscience, 3 (1990) 71–86
Ullman, S., Basri, R.: Recognition by Linear Combination of Models. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 13, 10 (1991) 992–1006
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
von der Heydt, R., Peterhans, E., Baumgartner G.: Illusory Contours and Cortical Neuron Responses. Science, 224 (1984) 1260–1262
Weber, M, Welling M. & Perona, P.: Towards Automatic Discovery of Object Categories. Proc. IEEE Comp. Soc. Conference on Computer Vision and Pattern Recognition, CVPR2000, 2, (2000) 101–108
Wiskott, L., Fellous J. M., Krüger N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. Intelligent Biometric Techniques in Fingerprint and Face Recognition, 11 (1999) 355–396
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ullman, S., Sali, E., Vidal-Naquet, M. (2001). A Fragment-Based Approach to Object Representation and Classification. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form 2001. IWVF 2001. Lecture Notes in Computer Science, vol 2059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45129-3_7
Download citation
DOI: https://doi.org/10.1007/3-540-45129-3_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42120-7
Online ISBN: 978-3-540-45129-7
eBook Packages: Springer Book Archive