Abstract
In this paper, we consider a problem that is originated in computer vision: determining an optimal testing strategy for the corner point detection problem that is a part of FAST algorithm [11,12]. The problem can be formulated as building a decision tree with the minimum average depth for a decision table with all discrete attributes. We experimentally compare performance of an exact algorithm based on dynamic programming and several greedy algorithms that differ in the attribute selection criterion.
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.
Similar content being viewed by others
References
Breiman, L., et al.: Classification and Regression Trees. Chapman & Hall, New York (1984)
Breitbart, Y., Reiter, A.: A branch-and-bound algorithm to obtain an optimal evaluation tree for monotonic boolean functions. Acta Inf. 4, 311–319 (1975)
Chakaravarthy, V.T., et al.: Decision trees for entity identification: approximation algorithms and hardness results. In: Proceedings of the 26-th ACM Symposium on Principles of Database Systems, pp. 53–62. ACM, New York (2007)
Garey, M.R.: Optimal binary identification procedures. SIAM Journal on Applied Mathematics 23, 173–186 (1972)
Heeringa, B., Adler, M.: Approximating optimal binary decision trees. Tech. Rep. 05-25, University of Massachusetts, Amherst (2005)
Hyafil, L., Rivest, R.: Constructing optimal binary decision trees is NP-complete. Information Processing Letters 5, 15–17 (1976)
Moret, B.E., Thomason, M., Gonzalez, R.C.: The activity of a variable and its relation to decision trees. ACM Trans. Program. Lang. Syst. 2, 580–595 (1980)
Moshkov, M.J.: Decision trees. Theory and applications Nizhni Novgorod University Publishers (1994) (in Russain)
Moshkov, M.J., Chikalov, I.V.: Consecutive optimization of decision trees concerning various complexity measures. Fundam. Inf. 61, 87–96 (2003)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)
Rosten, E., Drummond, T.: Fusing points and lines for high performance tracking. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1508–1511 (2005)
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)
Schumacher, H., Sevcik, K.C.: The synthetic approach to decision table conversion. Commun. ACM 19, 343–351 (1976)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alkhalid, A., Chikalov, I., Moshkov, M. (2011). Constructing an Optimal Decision Tree for FAST Corner Point Detection. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-24425-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24424-7
Online ISBN: 978-3-642-24425-4
eBook Packages: Computer ScienceComputer Science (R0)