The document discusses using random forests for skin detection from images. It provides an agenda for a presentation which includes an overview of the skin detection scheme using random forests, a refresher on random forests and R support, and results and continuing work. Code and the dataset are available online. Random forests show the best performance for skin detection compared to other models. The presenter's results are incomplete due to a small training set and they plan to use a parallel computing cluster going forward.

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)