Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers
Complex volume rendering tasks require high‐dimensional transfer functions, which are notoriously difficult to design. One solution to this is to learn transfer functions from scribbles that the user places in the volumetric domain in an intuitive and natural manner. In this paper, we explicitly mod...
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| Published in | Computer graphics forum Vol. 34; no. 3; pp. 111 - 120 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2015
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Complex volume rendering tasks require high‐dimensional transfer functions, which are notoriously difficult to design. One solution to this is to learn transfer functions from scribbles that the user places in the volumetric domain in an intuitive and natural manner. In this paper, we explicitly model and visualize the uncertainty in the resulting classification. To this end, we extend a previous intelligent system approach to volume rendering, and we systematically compare five supervised classification techniques – Gaussian Naive Bayes, k Nearest Neighbor, Support Vector Machines, Neural Networks, and Random Forests – with respect to probabilistic classification, support for multiple materials, interactive performance, robustness to unreliable input, and easy parameter tuning, which we identify as key requirements for the successful use in this application. Based on theoretical considerations, as well as quantitative and visual results on volume datasets from different sources and modalities, we conclude that, while no single classifier can be expected to outperform all others under all circumstances, random forests are a useful off‐the‐shelf technique that provides fast, easy, robust and accurate results in many scenarios. |
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| AbstractList | Complex volume rendering tasks require high-dimensional transfer functions, which are notoriously difficult to design. One solution to this is to learn transfer functions from scribbles that the user places in the volumetric domain in an intuitive and natural manner. In this paper, we explicitly model and visualize the uncertainty in the resulting classification. To this end, we extend a previous intelligent system approach to volume rendering, and we systematically compare five supervised classification techniques - Gaussian Naive Bayes, k Nearest Neighbor, Support Vector Machines, Neural Networks, and Random Forests - with respect to probabilistic classification, support for multiple materials, interactive performance, robustness to unreliable input, and easy parameter tuning, which we identify as key requirements for the successful use in this application. Based on theoretical considerations, as well as quantitative and visual results on volume datasets from different sources and modalities, we conclude that, while no single classifier can be expected to outperform all others under all circumstances, random forests are a useful off-the-shelf technique that provides fast, easy, robust and accurate results in many scenarios. |
| Author | Soundararajan, K. P. Schultz, T. |
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| CitedBy_id | crossref_primary_10_1016_j_cag_2016_06_007 crossref_primary_10_1007_s12650_019_00584_3 crossref_primary_10_3390_math12121885 crossref_primary_10_1016_j_cag_2017_10_002 crossref_primary_10_1109_TVCG_2021_3127918 crossref_primary_10_1109_TVCG_2016_2637333 crossref_primary_10_1111_cgf_12934 crossref_primary_10_1111_cgf_13663 crossref_primary_10_1109_TVCG_2017_2744078 crossref_primary_10_1007_s12650_021_00787_7 crossref_primary_10_1109_ACCESS_2021_3100429 crossref_primary_10_1109_TVCG_2018_2816059 |
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| Copyright | 2015 The Author(s) Computer Graphics Forum © 2015 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2015 The Eurographics Association and John Wiley & Sons Ltd. |
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MIT Press, 2002. 4 – reference: Teistler M., Breiman R.S., Liong S.M., Ho L.Y., Shahab A., Nowinski W.L.: Interactive definition of transfer functions in volume rendering based on image markers. Int. J. Computer Assisted Radiology and Surgery 2 (2007), 55-64. 2 – reference: Kniss J.M., Uitert R.V., Stephens A., Li G.-S., Tasdizen T., Hansen C.: Statistically quantitative volume visualization. In Proc. IEEE Visualization 2005 (2005), pp. 287-294. 2, 8 – reference: Hastie T., Tibshirani R., Friedman J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Springer Series in Statistics. Springer, 2011. 3, 4, 5 – reference: Ip C.Y., Varshney A., JaJa J.: Hierarchical exploration of volumes using multilevel segmentation of the intensity-gradient histograms. 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| Snippet | Complex volume rendering tasks require high‐dimensional transfer functions, which are notoriously difficult to design. One solution to this is to learn... Complex volume rendering tasks require high-dimensional transfer functions, which are notoriously difficult to design. One solution to this is to learn... |
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| SubjectTerms | Analysis Artificial intelligence Categories and Subject Descriptors (according to ACM CCS) Classification Classifiers Forests I.4.6 [Image Processing and Computer Vision]: Segmentation-Pixel classification Image processing systems Mathematical models Probabilistic methods Probability theory Rendering Studies Transfer functions |
| Title | Learning Probabilistic Transfer Functions: A Comparative Study of Classifiers |
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