Deep Discriminative and Shareable Feature Learning

Abstract

In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.

Related Papers

• Zhen Zuo, Gang Wang, Bing Shuai, Lifan Zhao, and Qingxiong Yang. “Exemplar based Deep Discriminative and Shareable Feature Learning for scene image classification.” Pattern Recognition (PR), Elsevier, 2015. [PDF]
• Zhen Zuo, Gang Wang, Bing Shuai, Lifan Zhao, Qingxiong Yang, and Xudong Jiang. “Learning Discriminative and Shareable Features for Scene Classification.” In Proceedings of the European Conference on Computer Vision (ECCV), pp. 552-568. Springer, 2014. [PDF] [Features]
• Zhen Zuo and Gang Wang. “Learning Discriminative Hierarchical Features for Object Recognition.” Signal Processing Letters (SPL), IEEE, 2014. [Link]
• Zhen Zuo and Gang Wang. “Recognizing trees at a distance with discriminative deep feature learning.” In Proceedings of the Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on, pp. 1-5. IEEE, 2013. [Link][Dataset]