Abstract
The generation of object proposals plays an important role in object detection. Most existing methods produce object proposals by using bottom-up cues, such as closed contour or superpixel. In this paper, we propose a novel method to improve the ranking of object proposals by combining bottom-up cues with top-down information of objectivity. Firstly, we utilize the bottom-up method to generate initial object proposals of the given test image. Then we retrieve its top-k similar images from training images set. Considering both appearance and spatial similarity between initial object proposals and the ground truth bounding boxes of these top-k similar images, we obtain the top-down guided scores of initial object proposals. Finally, the refined score of each initial object proposal is modeled as a fusion of the bottom-up score and the top-down score. Experiments show that our method achieves better performance compared with the state-of-art on the Pascal VOC2007 dataset.
Original language | English |
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Pages (from-to) | 20-27 |
Number of pages | 8 |
Journal | Signal Processing: Image Communication |
Volume | 56 |
DOIs | |
Publication status | Published - Aug 2017 |
Externally published | Yes |
Keywords
- Object detection
- Object proposals
- Object recognition