Normalized Cut Meets MRF
| Autor: | Ismail Ben Ayed, Yuri Boykov, Dmitrii Marin, Meng Tang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Normalization (statistics)
Markov random field Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology ComputingMethodologies_PATTERNRECOGNITION Kernel (image processing) Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition 0202 electrical engineering electronic engineering information engineering Combinatorial optimization 020201 artificial intelligence & image processing Segmentation Cluster analysis Algorithm |
| Zdroj: | Computer Vision – ECCV 2016 ISBN: 9783319464749 ECCV (2) |
| DOI: | 10.1007/978-3-319-46475-6_46 |
| Popis: | We propose a new segmentation or clustering model that combines Markov Random Field (MRF) and Normalized Cut (NC) objectives. Both NC and MRF models are widely used in machine learning and computer vision, but they were not combined before due to significant differences in the corresponding optimization, e.g. spectral relaxation and combinatorial max-flow techniques. On the one hand, we show that many common applications for multi-label MRF segmentation energies can benefit from a high-order NC term, e.g. enforcing balanced clustering of arbitrary high-dimensional image features combining color, texture, location, depth, motion, etc. On the other hand, standard NC applications benefit from an inclusion of common pairwise or higher-order MRF constraints, e.g. edge alignment, bin-consistency, label cost, etc. To address NC+MRF energy, we propose two efficient multi-label combinatorial optimization techniques, spectral cut and kernel cut, using new unary bounds for different NC formulations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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