boofcv.alg.scene

Class ClassifierKNearestNeighborsBow<T extends ImageBase,Desc extends TupleDesc>

java.lang.Object

boofcv.alg.scene.ClassifierKNearestNeighborsBow<T,Desc>

public class ClassifierKNearestNeighborsBow<T extends ImageBase,Desc extends TupleDesc>
extends java.lang.Object

Scene classification which uses bag-of-word model and K-Nearest Neighbors. Classification data consists of a labeled set of word histograms. Each word histogram was created by 1) extracting all the features inside an image which was known to belong to a specific scene, 2) the word which best matches each feature is found and added to the histogram, and 3) the histogram is then normalized so that it adds up to one.

When classifying an image its word histogram is computed the same way. Using the just computed histogram the K-nearest neighbors to its word histogram are found in the classification data. The most frequent scene (the mode) of the k-neighbors is the selected scene type of the image being considered.

Constructor Summary

Constructors

Constructor and Description
ClassifierKNearestNeighborsBow(NearestNeighbor<HistogramScene> nn, DescribeImageDense<T,Desc> describe, FeatureToWordHistogram<Desc> featureToHistogram) Configures internal algorithms.

Method Summary

Modifier and Type	Method and Description
int	classify(T image) Finds the scene which most resembles the provided image
void	setClassificationData(java.util.List<HistogramScene> memory, int numScenes) Provides a set of labeled word histograms to use to classify a new image
void	setNumNeighbors(int numNeighbors) Specifies the number of neighbors it should search for when classifying\

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ClassifierKNearestNeighborsBow

public ClassifierKNearestNeighborsBow(NearestNeighbor<HistogramScene> nn,
                                      DescribeImageDense<T,Desc> describe,
                                      FeatureToWordHistogram<Desc> featureToHistogram)

Configures internal algorithms.

Parameters:

nn - Used to perform nearest-neighbor search

describe - Computes the dense image features

featureToHistogram - Converts a set of features into a word histogram

Method Detail

setNumNeighbors

public void setNumNeighbors(int numNeighbors)

Specifies the number of neighbors it should search for when classifying\

setClassificationData

public void setClassificationData(java.util.List<HistogramScene> memory,
                                  int numScenes)

Provides a set of labeled word histograms to use to classify a new image

Parameters:

memory - labeled histograms

classify

public int classify(T image)

Finds the scene which most resembles the provided image

Parameters:

image - Image that's to be classified

Returns:

The index of the scene it most resembles