一种基于度量距离学习的图像检索方法

CBIR系统由于受图像低层特征的限制,制约了它的检索效果。机器学习和统计方法是一种有效的提高检索性能的方法,但通常需要大量的训练样本才能达到满意的检索精度。提出一种理想的距离度量函数,在对图像进行简单分类并提供少量训练样本的基础上,通过类的距离度量矩阵M的学习来考虑分量之间的相关性。这个度量导入二次最佳化问题的解,将训练样本类结构的倾斜最小化。试验结果表明,该方法能在学习样本极少的情况下提高检索的性能。

Distance Metric Learning for Content-based Image Retrieval

The performance of a content-based image retrieval(CBIR) system is inherently constrained by the low-level features adopted to represent the images in the database.Machine learning and statistical modeling approach is a simple yet effective technique for image retrieval,but the retrieval performance is often dependent on a large number of training data.The method presents an optimal metric distance function based on a simple cluster and a little training data and by learning a distance metric M that respects these relationships.This metric is optimal in the sense of global quadratic minimization,and can be obtained from the clusters in the training data in a supervised fashion.Experiments show that the learned metrics can be used to significantly improve image retrieval performance.