create_class_lut_knnT_create_class_lut_knnCreateClassLutKnnCreateClassLutKnncreate_class_lut_knn (算子)

名称

create_class_lut_knnT_create_class_lut_knnCreateClassLutKnnCreateClassLutKnncreate_class_lut_knn — 使用 k-最近邻分类器（k-NN）创建查找表来对字节图像进行分类。

签名

描述

create_class_lut_knncreate_class_lut_knnCreateClassLutKnnCreateClassLutKnnCreateClassLutKnncreate_class_lut_knn generates a look-up table (LUT) ClassLUTHandleClassLUTHandleClassLUTHandleClassLUTHandleclassLUTHandleclass_luthandle using the data of a trained k-nearest neighbors classifier (k-NN) KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleknnhandle to classify multi-channel byte images. By using this k-NN-based LUT classifier, the operator classify_image_class_knnclassify_image_class_knnClassifyImageClassKnnClassifyImageClassKnnClassifyImageClassKnnclassify_image_class_knn of the subsequent classification can be replaced by the operator classify_image_class_lutclassify_image_class_lutClassifyImageClassLutClassifyImageClassLutClassifyImageClassLutclassify_image_class_lut。The classification is speed up considerably, because the estimation of the class in every image point is no longer necessary since every possible response of the k-NN is stored in the LUT. For the generation of the LUT, the parameter NumDimNumDimNumDimNumDimnumDimnum_dim of called operator create_class_knncreate_class_knnCreateClassKnnCreateClassKnnCreateClassKnncreate_class_knn is important. The number of image channels the images must have to be classified is defined in NumDimNumDimNumDimNumDimnumDimnum_dim.

To create the LUT, all pixel values are classified with classify_class_knnclassify_class_knnClassifyClassKnnClassifyClassKnnClassifyClassKnnclassify_class_knn。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_knnclassify_image_class_knnClassifyImageClassKnnClassifyImageClassKnnClassifyImageClassKnnclassify_image_class_knn。

With 'bit_depth'"bit_depth""bit_depth""bit_depth""bit_depth""bit_depth" the accuracy of the classification, the required storage, and the runtime needed to create the LUT can be controlled.

The following parameters of the k-NN-based LUT classifier can be set with GenParamNameGenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and GenParamValueGenParamValueGenParamValueGenParamValuegenParamValuegen_param_value:

'bit_depth'"bit_depth""bit_depth""bit_depth""bit_depth""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'"bit_depth""bit_depth""bit_depth""bit_depth""bit_depth" <= 8). If the bit depth of the LUT is smaller ('bit_depth'"bit_depth""bit_depth""bit_depth""bit_depth""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 pixel combinations, where NumDimNumDimNumDimNumDimnumDimnum_dim denotes the dimension of the LUT, which is specified in create_class_knncreate_class_knnCreateClassKnnCreateClassKnnCreateClassKnncreate_class_knn。For example, for 'bit_depth'"bit_depth""bit_depth""bit_depth""bit_depth""bit_depth" = 7, NumDimNumDimNumDimNumDimnumDimnum_dim = 3, the classes of 8 pixel combinations are mapped in the same LUT entry. The LUT requires at most bytes of storage. For example, for NumDimNumDimNumDimNumDimnumDimnum_dim = 3, 'bit_depth'"bit_depth""bit_depth""bit_depth""bit_depth""bit_depth" = 8 and number of classes is smaller than 16, the LUT requires 8 MB of storage with internal storage optimization. The runtime for the classification in classify_image_class_lutclassify_image_class_lutClassifyImageClassLutClassifyImageClassLutClassifyImageClassLutclassify_image_class_lut becomes minimal if the LUT fits into the cache.

Typical values: 6,7,8

Default: 8

Restriction: 'bit_depth'"bit_depth""bit_depth""bit_depth""bit_depth""bit_depth" >= 1, 'bit_depth'"bit_depth""bit_depth""bit_depth""bit_depth""bit_depth" <= 8.

'rejection_threshold'"rejection_threshold""rejection_threshold""rejection_threshold""rejection_threshold""rejection_threshold"：

Threshold for the rejection of uncertain classified points of the k-NN. The parameter represents a threshold on the distance returned by the classification (see classify_class_knnclassify_class_knnClassifyClassKnnClassifyClassKnnClassifyClassKnnclassify_class_knn). All pixels having a distance over 'rejection_threshold'"rejection_threshold""rejection_threshold""rejection_threshold""rejection_threshold""rejection_threshold" are not assigned to any class.

Default: 5

Restriction: 'rejection_threshold'"rejection_threshold""rejection_threshold""rejection_threshold""rejection_threshold""rejection_threshold" >= 0.

执行信息

• 多线程类型：可重入（与非独占算子并行运行）。
• 多线程作用域：全局（可从任何线程调用）。
• 在内部数据级别上自动并行化。

此算子返回一个句柄。请注意，即使该句柄被用作特定算子的输入参数，这些算子仍可能改变此句柄类型的实例状态。

参数

结果

如果参数有效，算子 create_class_lut_knncreate_class_lut_knnCreateClassLutKnnCreateClassLutKnnCreateClassLutKnncreate_class_lut_knn 返回值 2 ( H_MSG_TRUE )。如有必要，则抛出异常。

可能的前趋

train_class_knntrain_class_knnTrainClassKnnTrainClassKnnTrainClassKnntrain_class_knn, read_class_knnread_class_knnReadClassKnnReadClassKnnReadClassKnnread_class_knn

可能的后继

classify_image_class_lutclassify_image_class_lutClassifyImageClassLutClassifyImageClassLutClassifyImageClassLutclassify_image_class_lut

替代

create_class_lut_svmcreate_class_lut_svmCreateClassLutSvmCreateClassLutSvmCreateClassLutSvmcreate_class_lut_svm, create_class_lut_gmmcreate_class_lut_gmmCreateClassLutGmmCreateClassLutGmmCreateClassLutGmmcreate_class_lut_gmm, create_class_lut_mlpcreate_class_lut_mlpCreateClassLutMlpCreateClassLutMlpCreateClassLutMlpcreate_class_lut_mlp

另见

classify_image_class_lutclassify_image_class_lutClassifyImageClassLutClassifyImageClassLutClassifyImageClassLutclassify_image_class_lut, clear_class_lutclear_class_lutClearClassLutClearClassLutClearClassLutclear_class_lut

模块

基础