2015/02/04 by Reza Farrahi Moghaddam, Mohamed Cheriet, Moghaddam, Reza Farrahi +1
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Remote-Sensing Image Classification #cs.CV
paper · pdf · doi:10.48550/arxiv.1502.01199
12 pages, 8 figures, 6 tables. Presented at ICDAR'15
openalex publication_date 2015/02/04 · arxiv created 2015/08/26 · arxiv updated 2015/08/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, a multiple-expert binarization framework for multispectral images is proposed. The framework is based on a constrained subspace selection limited to the spectral bands combined with state-of-the-art gray-level binarization methods. The framework uses a binarization wrapper to enhance the performance of the gray-level binarization. Nonlinear preprocessing of the individual spectral bands is used to enhance the textual information. An evolutionary optimizer is considered to obtain the optimal and some suboptimal 3-band subspaces from which an ensemble of experts is then formed. The framework is applied to a ground truth multispectral dataset with promising results. In addition, a generalization to the cross-validation approach is developed that not only evaluates generalizability of the framework, it also provides a practical instance of the selected experts that could be then applied to unseen inputs despite the small size of the given ground truth dataset.