a random forest approach to skin detection with r
DESCRIPTION
TRANSCRIPT
A RANDOM FOREST* APPROACH TO SKIN
DETECTION WITH RAuro Tripathy
*Random Forests are registered trademarks of Leo Breiman and Adele Cutler
Outline
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)
Attribution - Implementing an Existing Technique with R
ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5651638
R Code and Dataset
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
Tips to Prepare the Dataset
All training sets organized as a two-movie sequence
1. A movies sequence of frames in color2. A corresponding sequence of frames in binary
black-and-white, the ground-truth Extract individual frames in jpeg format
using ffmpeg, a transcoding toolffmpeg -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
Training Sample - Image and Corresponding Ground-truth
Ground-truthImage
The original authors used 8991 such image-pairs, the image along with its manually annotated pixel-level ground-truth.
Outline
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)
Problem Statement &Summary of Solution
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”
Choice of IHLS Color-Space
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
R Packages
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%
Pseudo Code
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 interfacerandomForest(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)
Outline
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)
Basics - Random forest is an ensemble classifier
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
Random Forest Classification Concept
Each decision tree votes for a classification
Forest chooses a classification with the
most votes
Benefits 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
Outline
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)
Authors’ ResultsShow Random Forest is Best-in-class!
My Results? OK, but incomplete due to very small training set.Need parallel computing cluster
ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5651638
Outline
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)