Saliency categories

Base classes which give a general interface for each specialized type of saliency algorithm and provide utility methods for each algorithm in its class.

StaticSaliency

class StaticSaliency

StaticSaliency class:

class CV_EXPORTS StaticSaliency : public virtual Saliency
{
 public:

  bool computeBinaryMap( const Mat& saliencyMap, Mat& binaryMap );

 protected:
  virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0;
};

StaticSaliency::computeBinaryMap

This function perform a binary map of given saliency map. This is obtained in this way:

In a first step, to improve the definition of interest areas and facilitate identification of targets, a segmentation by clustering is performed, using K-means algorithm. Then, to gain a binary representation of clustered saliency map, since values of the map can vary according to the characteristics of frame under analysis, it is not convenient to use a fixed threshold. So, Otsu’s algorithm is used, which assumes that the image to be thresholded contains two classes of pixels or bi-modal histograms (e.g. foreground and back-ground pixels); later on, the algorithm calculates the optimal threshold separating those two classes, so that their intra-class variance is minimal.

C++: bool computeBinaryMap(const Mat& saliencyMap, Mat& binaryMap)

Parameters:

• saliencyMap – the saliency map obtained through one of the specialized algorithms
• binaryMap – the binary map

MotionSaliency

class MotionSaliency

MotionSaliency class:

class CV_EXPORTS MotionSaliency : public virtual Saliency
{


 protected:
  virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0;
};

Objectness

class Objectness

Objectness class:

class CV_EXPORTS Objectness : public virtual Saliency
{

 protected:
  virtual bool computeSaliencyImpl( const InputArray image, OutputArray saliencyMap ) = 0;
};