Name
Abstract | ||
|---|---|---|
Nowadays, big imaging data are very common in many fields of study. As a result, detecting small objects in very large images is challenging and computationally demanding. Taking advantage of the intrinsic cumulative properties of the Fisher Score, we propose the Integral Fisher Score (IFS) for low-complexity and accurate object detection in big imaging data. The IFS, which is a multi-dimensional extension of the Integral Image, allows computing the Fisher Vector associated with a spatial region using only four operations. This considerably reduces the computational cost of searching for a small query object on a very large target image. Evaluations for the detection of small object on high-resolution HUB telescope and digital pathology images show that IFS attains a high accuracy with short processing times. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICIP.2018.8451677 | 2018 25th IEEE International Conference on Image Processing (ICIP) |
Keywords | Field | DocType |
Big data,Fisher Vectors,object detection,integral image,local features | Computer vision,Object detection,Telescope,Pattern recognition,Scoring algorithm,Computer science,Digital pathology,Feature extraction,Artificial intelligence,Frequency modulation,Big data,Detector | Conference |
ISSN | ISBN | Citations |
1522-4880 | 978-1-4799-7062-9 | 0 |
PageRank | References | Authors |
0.34 | 14 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Leyva | 1 | 20 | 3.80 |
| Victor Sanchez | 2 | 144 | 31.22 |
| Chang-Tsun Li | 3 | 937 | 72.14 |