From 67b1912916495e374c0246d41f87a720ba1aadf1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?C=C3=A9dric=20Traizet?= <traizetc@cesbio.cnes.fr>
Date: Tue, 27 Jun 2017 15:53:52 +0200
Subject: [PATCH] comments added/modified
---
README.md | 25 ++++++++++++++++++++++++
app/cbDimensionalityReduction.cxx | 32 +++++++++++++------------------
2 files changed, 38 insertions(+), 19 deletions(-)
diff --git a/README.md b/README.md
index e69de29bb2..a46b8fbf8a 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1,25 @@
+This module contains a new dimensionality reduction framework for the Orfeo Toolbox.
+
+The framework is based on Machine learning models and a dimensionality reduction algorithm
+can be trained and used using the model class, in the same fashion as Machine Learning Models
+for Classification (supervised or unspervised) and Regression.
+
+The algorithms featured in the module are (27/06/2017) :
+ - autoencoders and multi layer autoencoders, with several regularization options
+ - PCA
+ - SOM
+
+ Autoencoders and PCA models are using Shark ML library, while SOM model is based on the SOM classes of the OTB.
+
+ More specifically, the module contains :
+
+ - Autoencoder models, PCA models and SOM models, with methods for training, serialization and prediction (i.e. reduction)
+ - A Dimensionality Reduction Model Factory and a factories for each model, which allow the user to create objects of the model classes
+ - A (OTB ImageToImage) filter that can be used to perform dimensionality reduction on an image. This filter supports threading and streaming
+ - An application for training the models according to a shapefile
+ - An application for using a trained model on a shapefile
+ - An application for using a trained model on an image (using the filter)
+
+ /!\ Work In Progress /!\
+
+
diff --git a/app/cbDimensionalityReduction.cxx b/app/cbDimensionalityReduction.cxx
index 6d5366e9eb..106f609d48 100644
--- a/app/cbDimensionalityReduction.cxx
+++ b/app/cbDimensionalityReduction.cxx
@@ -112,33 +112,27 @@ protected:
private:
void DoInit() ITK_OVERRIDE
{
- SetName("PredictRegression");
- SetDescription("Performs a prediction of the input image according to a regression model file.");
+ SetName("DimensionalityReduction");
+ SetDescription("Performs dimensionality reduction of the input image according to a dimensionality reduction model file.");
// Documentation
- SetDocName("Predict Regression");
- SetDocLongDescription("This application predict output values from an input"
- " image, based on a regression model file produced by"
- " the TrainRegression application. Pixels of the "
- "output image will contain the predicted values from"
- "the regression model (single band). The input pixels"
+ SetDocName("DimensionalityReduction");
+ SetDocLongDescription("This application reduces the dimension of an input"
+ " image, based on a dimensionality reduction model file produced by"
+ " the DimensionalityReductionTrainer application. Pixels of the "
+ "output image will contain the reduced values from"
+ "the model. The input pixels"
" can be optionally centered and reduced according "
"to the statistics file produced by the "
- "ComputeImagesStatistics application. An optional "
- "input mask can be provided, in which case only "
- "input image pixels whose corresponding mask value "
- "is greater than 0 will be processed. The remaining"
- " of pixels will be given the value 0 in the output"
- " image.");
+ "ComputeImagesStatistics application. ");
SetDocLimitations("The input image must contain the feature bands used for"
- " the model training (without the predicted value). "
+ " the model training. "
"If a statistics file was used during training by the "
"TrainRegression, it is mandatory to use the same "
- "statistics file for prediction. If an input mask is "
- "used, its size must match the input image size.");
+ "statistics file for prediction.");
SetDocAuthors("OTB-Team");
- SetDocSeeAlso("TrainRegression, ComputeImagesStatistics");
+ SetDocSeeAlso("DimensionalityReductionTrainer, ComputeImagesStatistics");
AddDocTag(Tags::Learning);
@@ -187,7 +181,7 @@ private:
inImage->UpdateOutputInformation();
unsigned int nbFeatures = inImage->GetNumberOfComponentsPerPixel();
- // Load svm model
+ // Load DR model using a factory
otbAppLogINFO("Loading model");
m_Model = DimensionalityReductionModelFactoryType::CreateDimensionalityReductionModel(GetParameterString("model"),
DimensionalityReductionModelFactoryType::ReadMode);
--
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