Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition

Algorithm 2

Testing algorithm of CDFAG.

Input:

Raw features of testing samples in target dataset , the number of testing samples

in target dataset , dimension of raw features , dimension of common latent subspace , trade-off parameter ,

learned projection function for target dataset , trained aligned-to-generalized encoder for target dataset

and , SVM classifier trained by samples from both source and target dataset.

Feature alignment:

(1)Map the raw features in target datasets to the Hilbert space:

(2)Apply to map the raw features in target datasets to the new dimensional common latent space to obtain aligned features:

Feature generalization:

(3)Input the aligned features into the trained aligned-to-generalized encoder and obtained the generalized features

at the output layer:

Classification:

(4)Adopt the trained SVM classifier to predicts the class labels of testing samples in target dataset using generalized features

.

Output:

Predicted labels of testing samples in the target dataset.