基于平均检索精度的图像特征融合方法

Feature Fusion Based on the Average Precision in Image Retrieval

在基于内容的图像检索中,不同图像特征反映了图像不同侧面的内在特性,如何有效地组织和利用这些特征从而提高系统的检索性能是一个值得研究的课题.首先提出了特征互补率的定义,通过计算互补矩阵有指导地选择融合特征集.实验结果表明,互补矩阵能够很好地估计特征之间的补充能力.同时提出了基于平均检索精度的特征线性融合方法,并在一个包含12000张异质图像的大型图像库上与当前图像检索中最常用的几种方法进行了对比实验,结果表明这种方法具有更高的精度.

In content-based image retrieval, image has various inherent aspects which reflect its contents, therefore how to organize and utilize these contents effectively to improve the retrieval performance is a valuable research topic. In this paper, a method of measuring the complementarities between two feature spaces is proposed, based on which the fusion feature set can be selected effectually, and the experimental results are positive. At the same time, a linear fusion method based on the average precision of features is proposed. Extensive comparisons against several methods, such as flat model and rank-based linear fusion are performed. Experiments are carried out on a large-size heterogeneous image collection consisting of 12000 images and the results demonstrate the effectiveness of the proposed method.