Abstract
Efficient Euclidean minimum spanning tree algorithms have been proposed for large scale datasets which run typically in time near linear in the size of the data but may not usually be feasible for high-dimensional data. For data consisting of sparse vectors in high-dimensional feature spaces, however, the calculations of an approximate EMST can be largely independent of the feature space dimension. Taking this observation into consideration, in this paper, we propose a new two- stage approximate Euclidean minimum spanning tree algorithm. In the first stage, we perform the standard Prim’s MST algorithm using Cosine similarity measure for high-dimensional sparse datasets to reduce the computation expense. In the second stage, we use the MST obtained in the first stage to complete an approximate Euclidean Minimum Spanning Tree construction process. Experimental results for color image segmentation demonstrate the efficiency of the proposed method, while keeping high approximate precision.
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Acknowledgment
The authors would like to thank the Chinese National Science Foundation for its valuable support of this work under award 61473220 and all the anonymous reviewers for their valuable comments.
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Wang, X.L., Wang, X. (2018). An Efficient Approximate EMST Algorithm for Color Image Segmentation. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_11
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DOI: https://doi.org/10.1007/978-3-319-96133-0_11
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