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# 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;
    };

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