Isomap Based on the Image Euclidean Distance

Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (Isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the Isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images—the Image Euclidean distance based Isomap (ImIsomap), in which we use a new distance for images called IMage Euclidean Distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm ImIsomap over the traditional Isomap based on Euclidean distance. 1