Journal
PATTERN RECOGNITION
Volume 73, Issue -, Pages 1-14Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2017.06.036
Keywords
Convolutional neural networks (CNNs); Deep learning; Geographic image annotation; Multi-modal feature fusion
Funding
- China's National Key RD Program [2017YFB0503503]
- National Natural Science Foundation of China [61573284]
- Science and Technology Plan Project of Hunan Province [2016TP1020]
- HNU [15B22]
- Program of Key Disciplines in Hunan Province
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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 (DBN). It uses a restricted Boltzmann machine (RBM) to mine deep correlations between high-level features from both shallow and deep modalities to achieve a final representation for geographic images. Comprehensive experiments show that this feature fusion based method achieves much better performances compared to traditional methods. (C) 2017 Published by Elsevier Ltd.
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