基于成对约束Info-Kmeans聚类的图像索引方法

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。

Image indexing method based on clustering viaInfo-Kmeans under pair constraints

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.