Image classification: A hierarchical dictionary learning approach

• Published in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 2597 - 2601
• Main Authors: Mahdizadehaghdam, Shahin, Liyi Dai, Krim, Hamid, Skau, Erik, Han Wang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-03-2017
• Subjects: Atomic layer deposition; Dictionaries; dictionary learning; dimension reduction; Face; image classification; image processing; Learning systems; Optimization; sparse coding; Sparse matrices; Training
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2017.7952626

Hierarchical dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method to learn a hierarchy of two overcomplete synthesis dictionaries with an image classification goal. The classification objective in some sense regularizes the joint optimization of the hierarchical dictionaries and injects refinement feedback. The validation of the proposed approach is based on its classification performance using two well-known data sets.