关键词: 图像序列, 谱流形学习, 混合多流形, 局部切空间, 层次流形

Abstract: To better understand the hidden depth information of image sequence, people need to figure out the hidden data structure of the image sequence. At present, spectral manifold learning is efficient to learn the low dimensional embedding coordinates projected from the high dimensional sample data and thereby learning the manifold structure. Since the conventional method based on the hypothesis which the sample data are distributed on one single manifold, does not support hybrid multiple manifold model. Combined with the structure characteristics of image sequence, this paper presents a new algorithm called spectral deep learning (SDL) algorithm. Through setting up multimanifold mixed model, this algorithm preserves the continuity and smoothness of the local changes on the manifold. Through a method called manifold alignment, this algorithm establishes the mapping between the hierarchy manifold to find the deep low dimensional embedding coordinates of the image sequence. The experiments illustrate the validity of hybrid multiple manifold and image sequence data set of this algorithm．

Key words: image sequence, spectral manifold learning, hybrid multiple manifold, local tangent space, hierarchy  , manifold