A Random Forest Approach To Skin Detection With R

Auro Tripathy

[email protected]

*Random Forests are registered trademarks of Leo Breiman and Adele Cutler

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)

ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5651638

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

http://www.shatterline.com/SkinDetection.html

http://www.feeval.org/Data-sets/Skin_Colors.html



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

Ground-truth Image

The original authors used 8991 such image-pairs, the image along with its manually annotated pixel-level ground-truth.

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”

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

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%

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)

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

Each decision tree votes for a classification

Forest chooses a classification with the

most votes

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

My Results? OK, but incomplete due to very small training set. Need parallel computing cluster

ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5651638

A Random Forest Approach To Skin Detection With R

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