Image Classification With Tailored Fine-Grained Dictionaries

Keywords

Class-specific dictionaries (CSDs); dictionary learning; family-specific dictionaries (FSDs); image classification; universal dictionary (UD)

Abstract

In this paper, we propose a novel fine-grained dictionary learning method for image classification. To learn a high-quality discriminative dictionary, three types of multispecific subdictionaries, i.e., class-specific dictionaries (CSDs), universal dictionary (UD), and family-specific dictionaries (FSDs), are simultaneously uncovered. Here, CSDs and UD, respectively, model the patterns for each class and the patterns irrespective of any class. FSDs can help reveal the shared patterns between multiple image classes, by filling the gap between the patterns in CSDs and UD. The dependence among image classes is revealed by the shared FSDs, and a common FSD can be assigned to several classes to represent their residual. Finally, the most discriminative FSD for each class is identified by minimizing the sparse reconstruction error. Extensive experiments are conducted on different widely used data sets for image classification. The results demonstrate the superior performance of the proposed method over some state-of-the-art methods.

Publication Date

2-1-2018

Publication Title

IEEE Transactions on Circuits and Systems for Video Technology

Volume

28

Issue

2

Number of Pages

454-467

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TCSVT.2016.2607345

Socpus ID

85041951434 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85041951434

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