一种鲁棒的多特征彩色图像检索新方法

A Robust Content-based Color Image Retrieval Using Multiple Features

为了提高彩色图像检索的准确性,以回归型支持向量机(SVR)理论为基础,结合重要的图像边缘信息,提出了一种鲁棒的多特征彩色图像检索新方法。该方法首先利用回归型支持向量机(SVR)理论,对原始图像进行去噪处理及彩色边缘提取;然后将整个彩色边缘划分成局部网格区域,并分别计算出每个网格区域的颜色直方图和纹理直方图;最后综合利用上述网格区域的颜色直方图和纹理直方图来计算图像间内容的相似度,并进行彩色图像检索。实验结果表明,该方法不仅能够准确、快速的检索出用户所需图像,而且对光照、锐化、模糊等噪声攻击均具有较好的鲁棒性。

In this paper, a robust content-based color image retrieval algorithm using multiple features is proposed. This algorithm takes into consideration the important image edges and utilizes the support vector regression （SVR） theory. Firstly, the image denoising and color edge detection are performed using the SVR classification. Secondly, the whole color edge is divided into local grids; and the color histograms and texture histograms for local grids are computed as image features. Finally, the similarity between color images is computed by using a combined feature index based on the color histogram and texture histogram for local grids. Experimental experiments, including comparisons with many state-of-thearts, show the effectiveness of our algorithm in improving the retrieval performance（ especially for noisy images）.