Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two ap-proaches are feasible and practicable.
Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as "pandas eat bamboo". In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.
提出了基于三维人体运动数据和等距特征映射(ISOMAP)降维机制的子空间人体运动风格生成和编辑的方法.该方法拓展了传统ISOMAP难以处理非训练(out of sample)数据的局限性,在具有非线性内在属性的高维运动数据空间和低维非线性风格化子空间之间建立映射,使之能够直接应用在非训练数据集.对于映射到低维子空间的运动数据利用分解生成模型分离出运动的内容参数和风格参数,通过在子空间中调整这两种参数并逆向映射到原始运动数据空间,实现运动数据的编辑和新风格的生成.实验结果表明,该方法能够在虚拟现实场景中自动生成各种复杂新风格的人体运动,并具有精度高的特点.
