Search found 157 matches
- 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. ⋅&...
- 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....
- 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...
- 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...
- 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...
- 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 ...
- 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...
- 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...
- 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...
- 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...