| Full text | |
| Author(s): |
Hirata, Nina S. T.
[1]
Total Authors: 1
|
| Affiliation: | [1] Univ Sao Paulo, Dept Comp Sci, Inst Math & Stat, DCC IME USP, BR-05508090 Sao Paulo - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; v. 31, n. 4, p. 707-720, APR 2009. |
| Web of Science Citations: | 16 |
| Abstract | |
The design of binary morphological operators that are translation-invariant and locally defined by a finite neighborhood window corresponds to the problem of designing Boolean functions. As in any supervised classification problem, morphological operators designed from a training sample also suffer from overfitting. Large neighborhood tends to lead to performance degradation of the designed operator. This work proposes a multilevel design approach to deal with the issue of designing large neighborhood-based operators. The main idea is inspired by stacked generalization (a multilevel classifier design approach) and consists of, at each training level, combining the outcomes of the previous level operators. The final operator is a multilevel operator that ultimately depends on a larger neighborhood than of the individual operators that have been combined. Experimental results show that two-level operators obtained by combining operators designed on subwindows of a large window consistently outperform the single-level operators designed on the full window. They also show that iterating two-level operators is an effective multilevel approach to obtain better results. (AU) | |