The most commonly used shape similarity metrics are the sum of squared differences (SSD) and the sum of absolute differences (SAD). However, Maximum Likelihood (ML) theory allows us to relate the noise (differences between feature vectors) distribution more generally to a metric. In this paper, a shape is partitioned into tokens based on its concave regions, invariant moments are computed for each token, and token similarity is measured by a metric. Finally, a non-metric measure that employs heuristics is used to measure the shape similarity. The desirable property of this scheme is to mimic the human perception of shapes. We show that the ML metric outperforms the SSD and SAD metrics for token matching. Instead of the ML metric based on histograms for PDF approximation, which suffer from being sensitive to choices of bin width, we propose a Parzen windows method that is continuous and more robust. © Springer-Verlag 2004.
CITATION STYLE
Alajlan, N., Fieguth, P., & Kamel, M. (2004). Robust shape retrieval using maximum likelihood theory. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3211, 745–752. https://doi.org/10.1007/978-3-540-30125-7_92
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