期刊论文

Zou, Changqing

英文

Pattern Recognition

0031-3203

2018

73

1-14

10.1016/j.patcog.2017.06.036

We thank all the reviewers for their valuable comments and feedback. This work is supported in part by grants from the China’s National Key R&D Program (2017YFB0503503), National Natural Science Foundation of China (61573284), the Science and Technology Plan Project of Hunan Province (2016TP1020), HNU Start-up Grant (15B22), and the Program of Key Disciplines in Hunan Province.

本校为通讯机构

This paper presents a multi-modal feature fusion based framework to improve the geographic image annotation. To achieve effective representations of geographic images, the method leverages a low-to-high learning flow for both the deep and shallow modality features. It first extracts low-level features for each input image pixel, such as shallow modality features (SIFT, Color, and LBP) and deep modality features (CNNs). It then constructs mid-level features for each superpixel from low-level features. Finally it harvests high-level features from mid-level features by using deep belief networks ...