An Efficient Approximate EMST Algorithm for Color Image Segmentation
| Autor: | Xiaochun Wang, Xia Li Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Feature vector Cosine similarity 02 engineering and technology Minimum spanning tree Measure (mathematics) Euclidean distance Dimension (vector space) Feature (computer vision) 020204 information systems Euclidean minimum spanning tree 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm |
| Zdroj: | Machine Learning and Data Mining in Pattern Recognition ISBN: 9783319961323 MLDM (2) |
| DOI: | 10.1007/978-3-319-96133-0_11 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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