期刊
IEEE ACCESS
卷 7, 期 -, 页码 148882-148896出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2947006
关键词
Texture indexing; wavelet packets; minimum probability of error; complexity regularization; minimum cost tree pruning
资金
- Fondecyt [1170854]
- CONICYT-Chile
- Advance Center in Electrical and Electronic Engineering (AC3E), Basal Project [FB0008]
Wavelet Packets (WPs) bases are explored seeking new discriminative features for texture indexing. The task of WP feature design is formulated as a learning decision problem by selecting the filter-bank structure of a basis (within a WPs family) that offers an optimal balance between estimation and approximation errors. To address this problem, a computationally efficient algorithm is adopted that uses the tree-structure of the WPs collection and the Kullback-Leibler divergence as a discrimination criterion. The adaptive nature of the proposed solution is demonstrated in synthetic and real data scenarios. With synthetic data, we demonstrate that the proposed features can identify discriminative bands, which is not possible with standard wavelet decomposition. With data with real textures, we show performance improvements with respect to the conventional Wavelet-based decomposition used under the same conditions and model assumptions.
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