基于距离度量学习的DCT域JPEG图像检索

JPEG images retrieval in DCT domain based on Distance Metric Learning

由于特征有限,传统基于欧式距离的压缩域检索性能并不理想。本文引入距离度量学习技术,研究压缩域图像检索,提出了一种基于距离度量学习的离散余弦变换(DCT)域联合图像专家小组(JPEG)图像检索方法。首先,提出了一种更有效的DCT域特征提取方法;其次,运用距离度量学习技术训练出一个更加有效的度量矩阵进行检索。在Corel5000上的图像检索实验表明,新方法有效提高了检索准确度。

Due to limited features extracted from compression domain, the conventional Euclidean distance based retrieval performance in compressed-domain is not satisfactory. The Distance Metric Learning（DML） is introduced to compressed-domain images retrieval and a DML based Discrete Cosine Transform（DCT） domain retrieval for Joint Photographic Experts Group（JPEG） images is developed. Firstly, we propose a more effective DCT domain features extraction method, and then the DML is applied to train a more efficient metric matrix for retrieval. Retrieval experiment on Corel5000 images database demonstrates that the approach proposed can effectively improve the retrieval accuracy.