Multi-Level Discriminative Dictionary Learning With Application to Large Scale Image Classification

• Published in: IEEE transactions on image processing Vol. 24; no. 10; pp. 3109 - 3123
• Main Authors: Shen, Li, Sun, Gang, Huang, Qingming, Wang, Shuhui, Lin, Zhouchen, Wu, Enhua
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-10-2015
• Subjects: Accuracy; Computational modeling; Dictionaries; discriminative dictionary learning; Encoding; Feature extraction; hierarchical method; Image coding; large scale classification; sparse coding; Training; discriminative dictionary learning; hierarchical method; Sparse coding; large scale classification
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2015.2438548

The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.