摘要
针对蒙古文古籍图像检索领域中对同一查询关键词,不同的二值化算法对整体检索性能影响问题,提出一种基于马尔科夫随机场的蒙古文古籍图像二值化方法,从而提高蒙古文古籍图像的检索性能。利用马尔科夫随机场模型在灰度图像和二值图像之间建模,通过训练码本估计隐藏层的先验概率,并分析灰度图像的直方图估计可观察层的概率密度。利用这两种先验知识实现图像二值化。实验数据集为100页蒙古文《甘珠尔经》,为了验证本文所提方法的性能,实验采用R-Precision作为评价指标。实验结果表明,基于马尔科夫随机场的二值化方法不仅可以有效修复受损图像,还可以进一步提高其检索性能。
Aiming at the problem of the influence of the same query keyword and different binarization algorithms on the overall retrieval performance in historical Mongolian document images retrieval, this paper presents an image binarization method of historical Mongolian document based on Markov random field to improve the retrieval performance of historical Mongolian documents. The MRF model is used to model the gray level image and the binary image. The prior probability of the hidden-layer is estimated by the training codebook, and the probability density of the observable-layer is estimated by analyzing the histogram of the gray image. The two kinds of prior knowledge are used to realize image binarization. The experimental data set is 100-page Mongolian Kanjur. In order to verify the performance of the proposed method, R-Precision is used as the evaluation index. Experimental results show that the binarization method based on Markov random field can not only effectively repair the damaged image, but also can improve its retrieval performance.
出处
《计算机应用与软件》
2017年第4期207-212,共6页
Computer Applications and Software
基金
国家自然科学基金项目(71163029)
关键词
遗传算法
小生境
图像分割
阈值
Genetic algorithm
Niche
Image segmentation
Threshold
