Perceptual Feature Extraction

In this chapter,\(^\dagger\) we present a deep model-based and data-driven hybrid architecture (DMD) for feature extraction. First, we construct a deep learning pipeline for progressively learning image features from simple to complex. We mix this deep model-based pipeline with a data-driven pipeline, which extracts features from a large collection of unlabeled images. Sparse regularization is then performed on features extracted from both pipelines in an unsupervised way to obtain representative patches. Upon obtaining these patches, a supervised learning algorithm is employed to conduct object prediction. We present how DMD works and explain why it works more effectively than traditional models from both aspects of neuroscience and computational learning theory.