Name
Abstract | ||
|---|---|---|
Recent advances in image-based object recognition have exploited object proposals to speed up the detection process by reducing the search space. In this paper, we present a novel idea that utilizes true objectness and semantic image filtering (retrieved within the convolutional layers of a Convolutional Neural Network) to propose effective region proposals. Information learned in fully convolutional layers is used to reduce the number of proposals and enhance their localization by producing highly accurate bounding boxes. The greatest benefit of our method is that it can be integrated into any existing approach exploiting edge-based objectness to achieve consistently high recall across various intersection over union thresholds. Experiments on PASCAL VOC 2007 and ImageNet datasets demonstrate that our approach improves two existing state-of-the-art models with significantly high margins and pushes the boundaries of object proposal generation. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neucom.2017.05.071 | Neurocomputing |
Keywords | Field | DocType |
Object proposals,Region of interest,Object detection,Deep learning,Neural networks | Convolutional neural network,Computer science,Artificial intelligence,Deep learning,Artificial neural network,Speedup,Computer vision,Object detection,Pattern recognition,Filter (signal processing),Machine learning,Bounding overwatch,Cognitive neuroscience of visual object recognition | Journal |
Volume | ISSN | Citations |
266 | 0925-2312 | 6 |
PageRank | References | Authors |
0.43 | 38 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad-Adeel Waris | 1 | 40 | 2.83 |
| Alexandros Iosifidis | 2 | 841 | 72.43 |
| Moncef Gabbouj | 3 | 3282 | 386.30 |