Image summarizing based on multi-kernel learning and AP clustering
基于多核学习和AP聚类的图像摘要选取方法*

Image summarizing is a critical step in the interactive image retrieval. The traditional methods taking in the feature space may encounter with the feature fusion and bad clustering results. This paper developed a novel approach based on multi-kernel learning and AP(Affinity Propagation) clustering: firstly, getting the image similarity using multi-kernel function to fuse the diverse visual features; secondly, clustering the images into the compact classes with AP method; lastly, selecting the exemplars as the summarization for the whole image collection. The experiments show that the image summarization selected by the method above can give a good global overview of image collections and performance of retrieval.