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A hierarchical-structured dictionary learning for image classification

conference contribution
posted on 2023-01-27, 04:13 authored by J Yoon, Jinho ChoiJinho Choi, C D Yoo
This paper proposes a hierarchical-structured discriminative dictionary learning algorithm for image classification. Hierarchical structure of the overall dictionary is learned such that the upper-level dictionaries are specific in representing patterns common across a wide set of class images while lower-level dictionaries are specific in representing patterns localized to a narrow set of class images. Therefore the root dictionary can represent patterns common to all classes, while the leaf dictionaries can represent patterns specific only to a single distinct class. The learned dictionary is efficient in its use of the bases, and leads to a more discriminative representation than that led by previous dictionaries which is devoid of any structure and contains redundant bases. This hierarchical-structured dictionary is learned by solving a constraint optimization problem that minimized reconstruction error of a given image while using dictionaries in the hierarchical structure pertaining only to the class of the image. Sparse representation is pursued in addition, and it acts a regularizer to improve generalization. The representation is as distinct as the paths to each of the class in the hierarchical structure are divergent. To evaluate the effectness of the hierarchical-structured dictionary, classification is performed on three benchmark datasets: Extended Yale B database, Caltech 101 and Caltech 256 dataset, and based on a common features, the proposed algorithm performs better than other state-of-the-art dictionary learning algorithms.

History

Pagination

155 - 159

ISBN-13

9781479957514

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

2014 IEEE International Conference on Image Processing, ICIP 2014