A Random Forest Approach To Skin Detection With R

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.jpeg
ffmpeg -i 14_gt_500frames.avi -f image2 -ss 1.000 -vframes 1
14_gt_500frames_offset10s.jpeg

6. Image Ground-truth
The original authors used 8991 such image-pairs, the image along with
its 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 commonly
used ones, always has a small numerical value for near-
achromatic colours, and is completely independent of the
brightness function
A 3D-polar Coordinate Colour Representation Suitable for
Image, Analysis Allan Hanbury and Jean Serra
MATLAB routines implementing the RGB-to-IHLS and IHLS-to-RGB are
available at http://www.prip.tuwien.ac.at/˜hanbury.
R routines implementing the RGB-to-IHLS and IHLS-to-RGB are
available 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 a
classification 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=5651638
My 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)