基于成对约束Info-Kmeans聚类的图像索引方法

Image indexing method based on clustering via Info-Kmeans under pair constraints

针对图像数据噪声大和高维稀疏的特点,提出了一种基于噪声过滤和Info-Kmeans聚类的图像索引构建方法。首先,利用余弦兴趣模式过滤噪声。其次,提出了一种新的Info-Kmeans聚类算法,该算法不仅避免KL-divergence计算过程中的零值困境问题,还能融合以成对约束出现的先验知识。最后,在LFW和Oxford_5K 2个图像数据集上的实验表明:噪声过滤能显著提高聚类性能;Info-Kmeans比已有聚类工具具有更优越的性能。

Constructing high-quality content-based image indexing is fairly difficult due to the large amount of noise in the data set and the high-dimension and the sparseness of the image data. To meet this challenge, a novel noise-filtering and clustering was proposed using Info-Kmeans based image indexing construction method. Firstly, a noise-filtering method using the cosine interesting patterns was presented. Secondly, a novel Info-Kmeans algorithm was proposed which could avoid the zero-feature dilemma caused by the use of KL-divergence and exploit the prior knowledge in the form of pair constraints. The experimental results on the two image data sets, LFW and Oxford 5K, well demonstrate that： noise filter can improve the clustering performance remarkably and the novel Info-Kmeans algorithm yields better results than the existing clustering tool.