Artikel ; Online: BING
Computational Visual Media, Vol 5, Iss 1, Pp 3-
Binarized normed gradients for objectness estimation at 300fps
2019 Band 20
Abstract: ... normed gradients (BING), can be used for efficient objectness estimation, which requires only ... for improving BING’s localization performance, when used in multi-thresholding straddling expansion (MTSE) post ...
| Abstract | Abstract Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g., add, bitwise shift, etc.). To improve localization quality of the proposals while maintaining efficiency, we propose a novel fast segmentation method and demonstrate its effectiveness for improving BING’s localization performance, when used in multi-thresholding straddling expansion (MTSE) post-processing. On the challenging PASCAL VOC2007 dataset, using 1000 proposals per image and intersection-over-union threshold of 0.5, our proposal method achieves a 95.6% object detection rate and 78.6% mean average best overlap in less than 0.005 second per image. |
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| Schlagwörter | object proposals ; objectness ; visual attention ; category agnostic proposals ; Electronic computers. Computer science ; QA75.5-76.95 |
| Thema/Rubrik (Code) | 004 |
| Sprache | Englisch |
| Erscheinungsdatum | 2019-04-01T00:00:00Z |
| Verlag | SpringerOpen |
| Dokumenttyp | Artikel ; Online |
| Datenquelle | BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl) |
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