Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Of knights-and-drawbridges-nat-beha... by Auro Tripathy 432 views
- Image Analysis for Food Scientists by Kevin Mader 882 views
- Back-propagation Primer by Auro Tripathy 613 views
- GoogLeNet Insights by Auro Tripathy 2364 views
- Recurrent nets and sensors by Adam Gibson 782 views
- Real-Time Streaming Data Analysis w... by Numenta 1774 views

1,407 views

Published on

A Random Forest Approach To Skin Detection With R

No Downloads

Total views

1,407

On SlideShare

0

From Embeds

0

Number of Embeds

8

Shares

0

Downloads

26

Comments

0

Likes

1

No embeds

No notes for slide

- 1. Auro Tripathy auro@shatterline.com*Random Forests are registered trademarks of Leo Breiman and Adele Cutler
- 2. Attributions, code and dataset location (1 minute) Overview of the scheme (2 minutes) Refresher on Random Forest and R Support (2 minutes) Results and continuing work (1 minute) Q&A (1 minute and later)
- 3. ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5651638
- 4. R code available here; my contribution http://www.shatterline.com/SkinDetection.html Data set available here http://www.feeval.org/Data-sets/Skin_Colors.html Permission to use may be required
- 5. All training sets organized as a two-movie sequence 1. A movies sequence of frames in color 2. A corresponding sequence of frames in binary black-and-white, the ground-truth Extract individual frames in jpeg format using ffmpeg, a transcoding tool ffmpeg -i 14.avi -f image2 -ss 1.000 -vframes 1 14_500offset10s.jpegffmpeg -i 14_gt_500frames.avi -f image2 -ss 1.000 -vframes 1 14_gt_500frames_offset10s.jpeg
- 6. Image Ground-truthThe original authors used 8991 such image-pairs, the image along withits manually annotated pixel-level ground-truth.
- 7. Attributions, code and dataset location (1 minute) Overview of the scheme (2 minutes) Refresher on Random Forest and R Support (2 minutes) Results and continuing work (1 minute) Q&A (1 minute and later)
- 8. Skin-color classification/segmentation Uses Improved Hue, Saturation, Luminance (IHLS) color-space RBG values transformed to HLS HLS used as feature-vectors Original authors also experimented with Bayesian network, Multilayer Perceptron, SVM, AdaBoost (Adaptive Boosting), Naive Bayes, RBF network“Random Forest shows the best performance in terms of accuracy,precision and recall”
- 9. The most important property of this [IHLS] space is a “well-behaved” saturation coordinate which, in contrast to commonlyused ones, always has a small numerical value for near-achromatic colours, and is completely independent of thebrightness function A 3D-polar Coordinate Colour Representation Suitable for Image, Analysis Allan Hanbury and Jean SerraMATLAB routines implementing the RGB-to-IHLS and IHLS-to-RGB areavailable at http://www.prip.tuwien.ac.at/˜hanbury.R routines implementing the RGB-to-IHLS and IHLS-to-RGB areavailable at http://www.shatterline.com/SkinDetection.html
- 10. Package ‘ReadImages’ This package provides functions for reading JPEG and PNG ﬁles Package ‘randomForest’ Breiman and Cutler’s Classification and regression based on a forest of trees using random inputs. Package ‘foreach’ Support for the foreach looping construct Stretch goal to use %dopar%
- 11. set.seed(371)skin.rf <- foreach(i = c(1:nrow(training.frames.list)), .combine=combine,.packages=randomForest) %do%{ #Read the Image #transform from RGB to IHLS #Read the corresponding ground-truth image #data is ready, now apply random forest #not using the formula interface randomForest(table.data, y=table.truth, mtry = 2, importance = FALSE, proximity = FALSE, ntree=10, do.trace = 100)}table.pred.truth <- predict(skin.rf, test.table.data)
- 12. Attributions, code and dataset location (1 minute) Overview of the scheme (2 minutes) Refresher on Random Forest and R Support (2 minutes) Results and continuing work (1 minute) Q&A (1 minute and later)
- 13. Have lots of decision-tree learners Each learner’s training set is sampled independently – with replacement Add more randomness – at each node of the tree, the splitting attribute is selected from a randomly chosen sample of attributes
- 14. Each decision tree votes for a classification Forest chooses aclassification with the most votes
- 15. Quick training phase Trees can grow in parallel Trees have attractive computing properties For example… Computation cost of making a binary tree is low O(N Log N) Cost of using a tree is even lower – O(Log N) N is the number of data points Applies to balanced binary trees; decision trees often not balanced
- 16. Attributions, code and dataset location (1 minute) Overview of the scheme (2 minutes) Refresher on Random Forest and R Support (2 minutes) Results and continuing work (1 minute) Q&A (1 minute and later)
- 17. ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5651638My Results? OK, but incomplete due to very small training set.Need parallel computing cluster
- 18. Attributions, code and dataset location (1 minute) Overview of the scheme (2 minutes) Refresher on Random Forest and R Support (2 minutes) Results and continuing work (1 minute) Q&A (1 minute and later)

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment