【摘要】 实现了一种分等级的图像数据库自动语义分类系统,其中主要涉及白天、夜晚、日出/日落、室内、室外、建筑物以及风景等几个典型的语义信息。通过对图像的信息以及现有底层特征的分析,针对各级分类提取了一些判别能力好的特征。采用支持向量机(SVM)作为分类器;同时为了提高分类准确率,将支持向量机的输出改为概率形式,引入拒绝机制来拒绝一些置信度较低的样本。将Google检索出来的图像作为系统的测试样本取得了很好的分类结果,实验表明本系统可以对Google检索系统的结果进行一定的改进。更多还原

【Abstract】 A hierarchical automatic classification system of the image database has been realized, which concerns the following semantic information: Day, Night, Sunrise/Sunset, Indoor, Outdoor, Building and Landscape. Several discriminative features are selected through analyzing the image contents and comparing the present low-level features. The application of Support Vector Machines(SVMs)in the hierarchical semantic image classification is investigated. To improve the accuracy,a reject scheme is adopted in our system through revising the standard SVM output into the probabilistic output. The system also achieves good performance on testing sample from the Google retrieval result,so it can be thought a good filter for the image retrieval system.更多还原