The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Shape Quantization and Recognition with Randomized Trees
Abstract
We explore a new approach to shape recognition based on a virtually infinite family of binary features (queries) of the image data, designed to accommodate prior information about shape invariance and regularity. Each query corresponds to a spatial arrangement of several local topographic codes (or tags), which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are a natural partial ordering corresponding to increasing structure and complexity; semi-invariance, meaning that most shapes of a given class will answer the same way to two queries that are successive in the ordering; and stability, since the queries are not based on distinguished points and substructures. No classifier based on the full feature set can be evaluated, and it is impossible to determine a priori which arrangements are informative. Our approach is to select informative features and build tree classifiers at the same time by inductive learning. In effect, each tree provides an approximation to the full posterior where the features chosen depend on the branch that is traversed. Due to the number and nature of the queries, standard decision tree construction based on a fixed-length feature vector is not feasible. Instead we entertain only a small random sample of queries at each node, constrain their complexity to increase with tree depth, and grow multiple trees. The terminal nodes are labeled by estimates of the corresponding posterior distribution over shape classes. An image is classified by sending it down every tree and aggregating the resulting distributions. The method is applied to classifying handwritten digits and synthetic linear and nonlinear deformations of three hundred [Formula: see text] symbols. State-of-the-art error rates are achieved on the National Institute of Standards and Technology database of digits. The principal goal of the experiments on [Formula: see text] symbols is to analyze invariance, generalization error and related issues, and a comparison with artificial neural networks methods is presented in this context. [Figure: see text]
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Citations
Cites
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Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · cited 1×
“(See Geman et al., 1992, for a comprehensive treatment of the bias/variance dilemma; see also the discussions in Breiman, 1994; Kong & Dietterich, 1995; and Raudys & Jain, 1991.)”
Background
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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
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Segmentation and Recognition Using Structure from Motion Point Clouds
Brostow, G. J., Shotton, J., Fauqueur, J., & Cipolla, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_5 · Cited by 1,050
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Gomes, H. M., Barddal, J. P., Enembreck, F., & Bifet, A. (2018) · ACM Computing Surveys · doi:10.1145/3054925 · Cited by 579
Random Forests and Adaptive Nearest Neighbors
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Sarker, I. H., Kayes, A. S. M., & Watters, P. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0219-y · Cited by 287
Fleuret, F., & Geman, D. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011113216584 · Cited by 273
Self-citationDecision tree classifiers for automated medical diagnosis
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Deep Learning: A Bayesian Perspective
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DALSA: domain adaptation for supervised learning from sparsely annotated MR images
Goetz, M., Weber, C., Binczyk, F., Polanska, J., Tarnawski, R., Bobek-Billewicz, B., Koethe, U., Kleesiek, J., Stieltjes, B., & Maier-Hein, K. H. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2463078 · Cited by 98
Deep Differentiable Random Forests for Age Estimation
Shen, W., Guo, Y., Wang, Y., Zhao, K., Wang, B., & Yuille, A. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2937294 · Cited by 91
GeoF: Geodesic Forests for Learning Coupled Predictors
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Multimodal mild depression recognition based on EEG-EM synchronization acquisition network
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Metadata
- Authors
- Amit, Y., & Geman, D.
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 1997-10-01
- Volume
- 9
- Issue
- 7
- Pages
- 1545-1588
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2120240539
- Cited by
- 1,275
- References
- 3
- FWCI
- 7.77
- Type
- journal article
- Language
- en
- Open access
- closed
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