| PCTSignatures Class |
Class implementing PCT (position-color-texture) signature extraction as described in:
Martin Krulis, Jakub Lokoc, and Tomas Skopal. Efficient extraction of clustering-based feature signatures using GPU architectures. Multimedia Tools Appl., 75(13):8071–8103, 2016.
The algorithm is divided to a feature sampler and a clusterizer. Feature sampler produces samples at given set of coordinates. Clusterizer then produces clusters of these samples using k-means algorithm. Resulting set of clusters is the signature of the input image.
A signature is an array of SIGNATURE_DIMENSION-dimensional points.Used dimensions are: weight, x, y position; lab color, contrast, entropy.
Inheritance HierarchyNamespace: Emgu.CV.XFeatures2D
Assembly: Emgu.CV.World (in Emgu.CV.World.dll) Version: 3.3.0.2824 (3.3.0.2824)
SyntaxThe PCTSignatures type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | PCTSignatures(VectorOfPointF, VectorOfInt) |
Creates PCTSignatures algorithm using pre-generated sampling points and clusterization seeds indexes.
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| PCTSignatures(VectorOfPointF, Int32) |
Creates PCTSignatures algorithm using pre-generated sampling points and number of clusterization seeds. It uses the provided sampling points and generates its own clusterization seed indexes.
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| PCTSignatures(Int32, Int32, PCTSignaturesPointDistributionType) |
Creates PCTSignatures algorithm using sample and seed count. It generates its own sets of sampling points and clusterization seed indexes.
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Properties| Name | Description | |
|---|---|---|
 | ClusterMinSize |
This parameter multiplied by the index of iteration gives lower limit for cluster size. Clusters containing fewer points than specified by the limit have their centroid dismissed and points are reassigned.
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| DistanceFunction |
Distance function selector used for measuring distance between two points in k-means.
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| DropThreshold |
Remove centroids in k-means whose weight is lesser or equal to given threshold.
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| GrayscaleBits |
Color resolution of the greyscale bitmap represented in allocated bits (i.e., value 4 means that 16 shades of grey are used). The greyscale bitmap is used for computing contrast and entropy values.
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| IterationCount |
Number of iterations of the k-means clustering. We use fixed number of iterations, since the modified clustering is pruning clusters (not iteratively refining k clusters).
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| JoiningDistance |
Threshold euclidean distance between two centroids. If two cluster centers are closer than this distance, one of the centroid is dismissed and points are reassigned.
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| MaxClustersCount |
Maximal number of generated clusters. If the number is exceeded, the clusters are sorted by their weights and the smallest clusters are cropped.
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| Ptr |
Pointer to the unmanaged object
(Inherited from UnmanagedObject.) |
| WeightA |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
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| WeightB |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
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| WeightEntropy |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
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| WeightL |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
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| WeightX |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
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| WeightY |
Weights (multiplicative constants) that linearly stretch individual axes of the feature space. (x,y = position. L,a,b = color in CIE Lab space. c = contrast. e = entropy)
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| WindowRadius |
Size of the texture sampling window used to compute contrast and entropy. (center of the window is always in the pixel selected by x,y coordinates of the corresponding feature sample).
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Methods| Name | Description | |
|---|---|---|
| ComputeSignature |
Computes signature of given image.
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| Dispose |
The dispose function that implements IDisposable interface
(Inherited from DisposableObject.) |
 | DisposeObject |
Release the unmanaged memory associated with this PCTSignatures object
(Overrides DisposableObjectDisposeObject.) |
 | DrawSignature |
Draws signature in the source image and outputs the result. Signatures are visualized as a circle with radius based on signature weight and color based on signature color. Contrast and entropy are not visualized.
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| Equals | (Inherited from Object.) |
| Finalize |
Destructor
(Inherited from DisposableObject.) |
| GetHashCode | (Inherited from Object.) |
| GetType | (Inherited from Object.) |
| MemberwiseClone | (Inherited from Object.) |
| ReleaseManagedResources |
Release the managed resources. This function will be called during the disposal of the current object.
override ride this function if you need to call the Dispose() function on any managed IDisposable object created by the current object
(Inherited from DisposableObject.) |
| ToString | (Inherited from Object.) |
Fields| Name | Description | |
|---|---|---|
 | _ptr |
A pointer to the unmanaged object
(Inherited from UnmanagedObject.) |
See Also