create_class_lut_svm🔗
Short description🔗
create_class_lut_svm — Create a look-up table using a Support-Vector-Machine to classify byte
images.
Signature🔗
create_class_lut_svm( class_svm SVMHandle, attribute.name GenParamName, attribute.value GenParamValue, out class_lut ClassLUTHandle )
Description🔗
create_class_lut_svm generates a look-up table (LUT)
ClassLUTHandle using the data of a trained Support-Vector-Machine
(SVM) SVMHandle to classify multi-channel byte images. By using
this SVM-based LUT classifier the operator classify_image_class_svm
of the subsequent classification can be replaced by the operator
classify_image_class_lut. The classification gets a major speed-up,
because the estimation of the class in every image point is no longer
necessary since every possible response of the SVM is stored in the LUT.
For the generation of the LUT, the parameters NumFeatures,
Preprocessing, and NumComponents defined in the earlier
called operator create_class_svm are important.
In NumFeatures, the number of image channels the images must have
to be classified is defined. By using the Preprocessing
(see create_class_svm) the number of image channels can be
transformed to NumComponents. NumComponents defines
the length of the feature vector, which the classifier
classify_class_svm handles internally. Because of performance and
disk space, the LUT is restricted to be maximal 3-dimensional. Since
it replaces the operator classify_class_svm,
NumComponents \(<=\) 3 must hold.
If there is no preprocessing that reduces the number of image channels
(NumFeatures \(=\) NumComponents), all possible pixel values,
which can occur in a byte image, are classified with
classify_class_svm. The returned classes are stored in the LUT.
If there is a preprocessing that reduces the number of image channels
(NumFeatures \(>\) NumComponents), the preprocessing
parameters of the SVM are stored in a separate structure of the LUT. To
create the LUT, all transformed pixel values are classified with
classify_class_svm.
The returned classes are stored in the LUT. Because of the discretization
of the LUT, the accuracy of the LUT classifier could become lower than the
accuracy of classify_image_class_svm.
With 'bit_depth' and 'class_selection' the accuracy of
the classification, the required storage, and the runtime needed to create
the LUT can be controlled.
The following parameters of the SVM-based LUT classifier can be set with
GenParamName and GenParamValue:
-
'bit_depth': Number of bits used from the pixels. It controls the storage requirement of the LUT classifier and is bounded by the bit depth of the image ('bit_depth' \(<=\) 8). If the bit depth of the LUT is smaller ('bit_depth' \(<\) 8), the classes of multiple pixel combinations will be mapped to the same LUT entry, which can result in a lower accuracy for the classification. One of these clusters contains \(2^{NumComponents \cdot (8-bit\_depth)}\) pixel combinations, where
NumComponentsdenotes the dimension of the LUT, which is specified increate_class_svm. For example, for 'bit_depth' \(=\) 7,NumComponents\(=\) 3, the classes of 8 pixel combinations are mapped in the same LUT entry. The LUT requires at most \(2^{NumComponents \cdot bit\_depth+2}\) bytes of storage. For example, forNumComponents\(=\) 3, 'bit_depth' \(=\) 8 andNumClasses\(<\) 16 (specified increate_class_svm), the LUT requires 8 MB of storage with internal storage optimization. IfNumClasses\(=\) 1, the LUT requires only 2 MB of storage by using the full bit depth of the LUT. The runtime for the classification inclassify_image_class_lutbecomes minimal if the LUT fits into the cache.Suggested values: 6,7,8
Default: 8
Restriction: 'bit_depth' \(>=\) 1, 'bit_depth' \(<=\) 8.
-
'class_selection': Method for the class selection for the LUT. Can be modified to control the accuracy and the runtime needed to create the LUT classifier. The value in 'class_selection' is ignored if the bit depth of the LUT is maximal, thus 'bit_depth' \(=\) 8 holds. If the bit depth of the LUT is smaller ('bit_depth' \(<\) 8), the classes of multiple pixel combinations will be mapped to the same LUT entry. One of these clusters contains \(2^{NumComponents \cdot (8-bit\_depth)}\) pixel combinations, where
NumComponentsdenotes the dimension of the LUT, which is specified increate_class_svm. By choosing 'class_selection' \(=\) 'best', the class that appears most often in the cluster is stored in the LUT. For 'class_selection' \(=\) 'fast', only one pixel of the cluster, i.e., the pixel with the smallest value (component-wise), is classified. The returned class is stored in the LUT. In this case, the accuracy of the subsequent classification could become lower. On the other hand, the runtime needed to create the LUT can be reduced, which is proportional to the maximal needed storage of the LUT, which is defined with \(2^{NumComponents \cdot bit\_depth+2}\).List of values: 'fast', 'best'
Default: 'fast'
Execution information🔗
Execution information
-
Multithreading type: reentrant (runs in parallel with non-exclusive operators).
-
Multithreading scope: global (may be called from any thread).
-
Automatically parallelized on internal data level.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
Parameters🔗
SVMHandle (input_control) class_svm → (handle)
SVM handle.
GenParamName (input_control) attribute.name-array → (string)
Names of the generic parameters that can be adjusted for the LUT classifier creation.
Default: []
Suggested values: 'bit_depth', 'class_selection'
GenParamValue (input_control) attribute.value-array → (string / integer)
Values of the generic parameters that can be adjusted for the LUT classifier creation.
Default: []
Suggested values: 8, 7, 6, 'fast', 'best'
ClassLUTHandle (output_control) class_lut → (handle)
Handle of the LUT classifier.
Result🔗
If the parameters are valid, the operator create_class_lut_svm
returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.
Combinations with other operators🔗
Combinations
Possible predecessors
train_class_svm, read_class_svm
Possible successors
Alternatives
create_class_lut_gmm, create_class_lut_knn, create_class_lut_mlp
See also
Module🔗
Foundation