Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification
This addresses scene image classification for computer vision applications, but it is incremental as it builds on existing feature learning methods.
The paper tackled scene image classification by proposing Deep Discriminative and Shareable Feature Learning (DDSFL) to learn hierarchical feature transformations that encode class correlations and discriminative information, achieving very promising performance and showing great complementary effect to state-of-the-art Caffe features.
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.