org.openimaj.feature

Enum IntFVComparison

• Enum Constant Summary

Enum Constants
Enum Constant and Description
`ARCCOS`
The arccosine of the cosine similarity
`BHATTACHARYYA`
Bhattacharyya distance d(H1,H2) = sqrt( 1 - (1 / sqrt(sumI(H1(I)) * sumI(H2(I))) ) * sumI( sqrt(H1(I) * H2(I)) ) )
`CHI_SQUARE`
Chi-squared distance d(H1,H2) = 0.5 * sumI[(H1(I)-H2(I))^2 / (H1(I)+H2(I))]
`CITY_BLOCK`
City-block (L1) distance d(H1,H2) = sumI( abs(H1(I)-H2(I)) )
`CORRELATION`
Correlation s(H1,H2) = sumI( H'1(I) * H'2(I) ) / sqrt( sumI[H'1(I)2]^2 * sumI[H'2(I)^2] ) where H'k(I) = Hk(I) - (1/N) * sumJ( Hk(J) ); N=number of FeatureVector bins
`COSINE_DIST`
Cosine distance (-COSINE_SIM)
`COSINE_SIM`
Cosine similarity (sim of 1 means identical) s(H1,H2)=sumI(H1(I) * H2(I))) / (sumI(H1(I)^2) sumI(H2(I)^2))
`EUCLIDEAN`
Euclidean distance d(H1,H2) = Math.sqrt( sumI( (H1(I)-H2(I))^2 ) )
`HAMMING`
Hamming Distance d(H1,H2) = sumI(H1(I) == H2(I) ? 1 : 0)
`INNER_PRODUCT`
Inner product s(H1,H2)=sumI(H1(I) * H2(I))
`INTERSECTION`
Histogram intersection s(H1,H2) = sumI( min(H1(I), H2(I)) )
`JACCARD_DISTANCE`
Jaccard distance.
`PACKED_HAMMING`
Hamming Distance for packed bit strings d(H1,H2) = sumI(H1(I) == H2(I) ? 1 : 0)
`SUM_SQUARE`
Sum-square distance d(H1,H2) = sumI( (H1(I)-H2(I))^2 )
`SYMMETRIC_KL_DIVERGENCE`
The symmetric Kullback-Leibler divergence.
• Method Summary

All Methods
Modifier and Type Method and Description
`abstract double` ```compare(int[] h1, int[] h2)```
Compare two feature vectors in the form of native arrays, returning a score or distance.
`double` ```compare(IntFV h1, IntFV h2)```
Compare two objects, returning a score or distance.
`boolean` `isDistance()`
`static IntFVComparison` `valueOf(String name)`
Returns the enum constant of this type with the specified name.
`static IntFVComparison[]` `values()`
Returns an array containing the constants of this enum type, in the order they are declared.
• Methods inherited from class java.lang.Enum

`clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf`
• Methods inherited from class java.lang.Object

`getClass, notify, notifyAll, wait, wait, wait`
• Enum Constant Detail

• EUCLIDEAN

`public static final IntFVComparison EUCLIDEAN`
Euclidean distance d(H1,H2) = Math.sqrt( sumI( (H1(I)-H2(I))^2 ) )
• SUM_SQUARE

`public static final IntFVComparison SUM_SQUARE`
Sum-square distance d(H1,H2) = sumI( (H1(I)-H2(I))^2 )
• CORRELATION

`public static final IntFVComparison CORRELATION`
Correlation s(H1,H2) = sumI( H'1(I) * H'2(I) ) / sqrt( sumI[H'1(I)2]^2 * sumI[H'2(I)^2] ) where H'k(I) = Hk(I) - (1/N) * sumJ( Hk(J) ); N=number of FeatureVector bins
• CHI_SQUARE

`public static final IntFVComparison CHI_SQUARE`
Chi-squared distance d(H1,H2) = 0.5 * sumI[(H1(I)-H2(I))^2 / (H1(I)+H2(I))]
• INTERSECTION

`public static final IntFVComparison INTERSECTION`
Histogram intersection s(H1,H2) = sumI( min(H1(I), H2(I)) )
• BHATTACHARYYA

`public static final IntFVComparison BHATTACHARYYA`
Bhattacharyya distance d(H1,H2) = sqrt( 1 - (1 / sqrt(sumI(H1(I)) * sumI(H2(I))) ) * sumI( sqrt(H1(I) * H2(I)) ) )
• HAMMING

`public static final IntFVComparison HAMMING`
Hamming Distance d(H1,H2) = sumI(H1(I) == H2(I) ? 1 : 0)
• PACKED_HAMMING

`public static final IntFVComparison PACKED_HAMMING`
Hamming Distance for packed bit strings d(H1,H2) = sumI(H1(I) == H2(I) ? 1 : 0)
• CITY_BLOCK

`public static final IntFVComparison CITY_BLOCK`
City-block (L1) distance d(H1,H2) = sumI( abs(H1(I)-H2(I)) )
• COSINE_SIM

`public static final IntFVComparison COSINE_SIM`
Cosine similarity (sim of 1 means identical) s(H1,H2)=sumI(H1(I) * H2(I))) / (sumI(H1(I)^2) sumI(H2(I)^2))
• COSINE_DIST

`public static final IntFVComparison COSINE_DIST`
Cosine distance (-COSINE_SIM)
• ARCCOS

`public static final IntFVComparison ARCCOS`
The arccosine of the cosine similarity
• SYMMETRIC_KL_DIVERGENCE

`public static final IntFVComparison SYMMETRIC_KL_DIVERGENCE`
The symmetric Kullback-Leibler divergence. Vectors must only contain positive values; internally they will be converted to double arrays and normalised to sum to unit length.
• JACCARD_DISTANCE

`public static final IntFVComparison JACCARD_DISTANCE`
Jaccard distance. Converts each vector to a set for comparison.
• INNER_PRODUCT

`public static final IntFVComparison INNER_PRODUCT`
Inner product s(H1,H2)=sumI(H1(I) * H2(I))
• Method Detail

• values

`public static IntFVComparison[] values()`
Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:
```for (IntFVComparison c : IntFVComparison.values())
System.out.println(c);
```
Returns:
an array containing the constants of this enum type, in the order they are declared
• valueOf

`public static IntFVComparison valueOf(String name)`
Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)
Parameters:
`name` - the name of the enum constant to be returned.
Returns:
the enum constant with the specified name
Throws:
`IllegalArgumentException` - if this enum type has no constant with the specified name
`NullPointerException` - if the argument is null
• isDistance

`public boolean isDistance()`
Specified by:
`isDistance` in interface `DistanceComparator<IntFV>`
Returns:
true if the comparison is a distance; false if similarity.
• compare

```public double compare(IntFV h1,
IntFV h2)```
Description copied from interface: `DistanceComparator`
Compare two objects, returning a score or distance.
Specified by:
`compare` in interface `DistanceComparator<IntFV>`
Parameters:
`h1` - the first object
`h2` - the second object
Returns:
a score or distance
• compare

```public abstract double compare(int[] h1,
int[] h2)```
Compare two feature vectors in the form of native arrays, returning a score or distance.
Specified by:
`compare` in interface `IntFVComparator`
Parameters:
`h1` - the first feature array
`h2` - the second feature array
Returns:
a score or distance