Accurate object retrieval for high-resolution remote-sensing imagery using high-order topic consistency potentials

Abstract

We propose incorporating semantic topic information into a hierarchical conditional random fields (CRFs) framework to promote object recognition and retrieval accuracy. Specially, we devise convenient yet effective methods based on multiple segmentations to perform accurate image retrieval tasks for rigid and amorphous man-made objects. Through a robust topic consistency potential (RTCP) modelling approach, we perform accurate multi-class segmentation on high-resolution remote-sensing images. The generated segments can be readily used for object recognition and discovery. We report satisfactory the performance on two sets of high-resolution remote-sensing images that cover a highly populated urban area and a rural area, respectively. Experimental results demonstrate that our approach outperforms the state-of-the-art CRF models, due to its ability to capture inherent semantic information for efficient object recognition and boundary discovery.

Additional information

Funding

This work was supported by National Basic Research Program of China [grant 2012CB719906]; National Natural Science Foundation of China [grant 41271400]; the Fundamental Research Funds for the Central Universities [grant 13CX02034A]; ‘3551 Optics Valley Talents Scheme’ of Wuhan East Lake High-tech Zone.