Search found 157 matches

by Creator
Tue Jan 26, 2016, 01:34
Forum: Direct Graphical Models
Topic: DGM lib for Ubuntu
Replies: 6
Views: 15011

Re: DGM lib for Ubuntu

The DGM library was never built in Linux. However, it is written, using only C++, so theoretically it might be ported to Ubuntu. You may take one of the options: ⋅  Write a makefile for the library and then use make command in Ubuntu in order to build it. This link might help here. ⋅&...
by Creator
Fri Jun 07, 2013, 22:04
Forum: Direct Graphical Models
Topic: Training of a random model.
Replies: 10
Views: 50545

Microsoft Sherwood Random Forest Model

Another Random Forest implementation is taken from the Microsoft Sherwood library . Its results for our test setup are depicted at Fig. 8 . These results are not much differ from the results, depicted in the Fig. 7 , nevertheless, the classification accuracy may be different from dataset to dataset....
by Creator
Fri Jan 25, 2013, 15:50
Forum: Direct Graphical Models
Topic: Training of a random model.
Replies: 10
Views: 50545

OpenCV Random Forest Model

One more example of OpenCV training approach - Random Forest (RF). Its results for our test setup are depicted at Fig. 7 . It has the same drawback as OpenCV GMM approach - all the training samples must be kept in memory for training. It is also very slow, but it is shown to produce good classificat...
by Creator
Tue Dec 25, 2012, 19:08
Forum: Direct Graphical Models
Topic: Training of a random model.
Replies: 10
Views: 50545

OpenCV Gaussian Mixture Model

The same as above, it is also possible to make use of the OpenCV implementation of the GMM. It is based on the Expectation Maximization (EM) method and produces the results, depicted at Fig. 6 (Each class is approximated with 16 Gaussians, default parameters). In comparison to our sequential GMM app...
by Creator
Tue Oct 23, 2012, 17:06
Forum: Direct Graphical Models
Topic: Training of a random model.
Replies: 10
Views: 50545

Gaussian Mixture Model

In spite of the Gaussian Model can encode the inter-feature dependences, it may produce even worse results as Bayes model. Approximating a complex form of real distributions is sometimes almost impossible with a single Gaussian function. In that reason we can extend this model by substituting a sing...
by Creator
Thu Jun 21, 2012, 01:13
Forum: Direct Graphical Models
Topic: Training of a random model.
Replies: 10
Views: 50545

Gaussian Model

Using Bayes Model in training we gain high performance. Nevertheless we lose all the inter-feature dependencies, i.e. each feature influences the resulting potential independently from all other features. It is also possible to use approximation, which is free from that drawback, e.g. approximation ...
by Creator
Wed Jun 20, 2012, 23:28
Forum: Direct Graphical Models
Topic: Training of a random model.
Replies: 10
Views: 50545

Bayes Model

In order to reconstruct the ortogonal n-dimensional PDF function from n one-dimensional PDFs, we make use of the superposition of them: PDF[ featureVector ][ state ] = MUL _{feature \in featureVector} ( H[ feature ][ state ] ); for (int state = 0; state < nStates; state++) { PDF[state] = 1; for (int...
by Creator
Tue Jun 19, 2012, 02:45
Forum: Direct Graphical Models
Topic: Training of a random model.
Replies: 10
Views: 50545

Bayes Model

The Bayes model approximates the PDFs via decomposing n-Dimensional space into n one-Dimensional signals. For this purpose we can build the 1-Dimensional PDFs for each feature and for each state (class), neglecting all the dependensies between features. These 1-Dimensional PDFs are histohrams H[ fea...
by Creator
Mon Jun 18, 2012, 16:08
Forum: Direct Graphical Models
Topic: Classification of Crossroads Using Conditional Random Fields
Replies: 0
Views: 10947

Classification of Crossroads Using Conditional Random Fields

This is my second scientific paper on random fields. ⋅ S. Kosov, F. Rottensteiner: "3D Classification of Crossroads from Multiple Aerial Images Using Conditional Random Fields" Pattern Recognition in Remote Sensing (PRRS 2012) Tsukuba Science City, Japan, November 2012 ⋅&nbs...
by Creator
Thu Jun 07, 2012, 18:16
Forum: Direct Graphical Models
Topic: Training of a random model.
Replies: 10
Views: 50545

Training of a random model.

Say, we have 3 different classes, in which we want to classify input data. We derive 2 features from the data, so the feature vector has length of two values. For simplicity, we describe each feature value with a 8-bit value, so it lies within the integer interval from 0 till 255. Having the groundt...

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