综合颜色和形状特征聚类的图像检索

为了提高图像检索的速度和准确率,通过分析各种聚类算法在图像检索中的缺点,提出了一种新的划分聚类的图像检索方法。首先对HSV模型非均匀量化,利用改进的颜色聚合向量方法提取图像的颜色特征；然后基于改进的Hu不变矩提取图像的全局形状特征；最后,综合颜色和形状特征对图像基于贡献度聚类并建立特征索引库。利用上述方法在Corel图像库中进行图像检索。实验结果表明,与改进的K-means算法的图像检索算法相比,提出算法的查准率和查全率均有较大提高。

Image retrieval based on clustering according to color and shape features

In order to improve the speed and accuracy of image retrieval, the drawbacks of image retrieval based on a variety of clustering algorithms were analyzed, then a new partition clustering method for image retrieval was presented in this paper. First, based on the asymmetrical quantization of the color in HSV model, color feature of image was extracted by color coherence vectors. Then, global shape feature of image was extracted based on improved Hu invariant moment. Finally,images were clustered based on contribution according to color and shape features, and image feature index library was established. The methods described above were used for image retrieval based Corel image library. The experimental results show that compared with image retrieval algorithms based on improved K-means algorithms, precision ratio and recall ratio of the proposed algorithm are improved greatly.