Direct Graphical Models
v.1.7.0

Submodule containing classes and methods for evaluation the classification results. More...
Classes  
class  DirectGraphicalModels::CCMat 
Confusion matrix class. More...  
Functions  
float  DirectGraphicalModels::getAveragePrecision (const vec_byte_t &predictions, const vec_float_t &potentials, const vec_byte_t >, byte state) 
Returns Average Precision for the selected state (class) state. More...  
Submodule containing classes and methods for evaluation the classification results.
float DirectGraphicalModels::getAveragePrecision  (  const vec_byte_t &  predictions, 
const vec_float_t &  potentials,  
const vec_byte_t &  gt,  
byte  state  
) 
Returns Average Precision for the selected state (class) state.
This function analyses 3 values of every image pixel, namely the predicted state, the groundtruth state and the potential of the pixel to be the class state, passed to the function via the arguments: predictions, gt and potentials, respectively. First, the pixels are sorted by the potentials in descending order. After that, the following algorithm is applied:
where AP stays for Average Precision.
predictions  The most probable configuration, returned by the CDecode::decode() function 
potentials  The potential values for each node of the graph, returned by the CInfer::getPotentials(state) function 
gt  The groundtruth values for each node of the graph. May be converted from a groundtruth image as follows: vec_byte_t gt(gtImg.data, gtImg.data + gtImg.cols * gtImg.rows); 
state  The state (class) for which the Average Precision is calculated 
Definition at line 6 of file AveragePrecision.cpp.