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Detecting small objects in high resolution images with integral fisher score
conference contribution
posted on 2018-01-01, 00:00 authored by R Leyva, V Sanchez, Chang-Tsun LiChang-Tsun LiNowadays, 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.
History
Event
IEEE Signal Processing Society. Conference (25th : 2018 : Athens, Greece)Series
IEEE Signal Processing Society ConferencePagination
316 - 320Publisher
Institute of Electrical and Electronics EngineersLocation
Athens, GreecePlace of publication
Piscataway, N.J.Publisher DOI
Start date
2018-10-07End date
2018-10-10ISSN
1522-4880ISBN-13
9781479970612Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2018, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
ICIP 2018 : Proceedings of the 2018 25th IEEE International Conference on Image ProcessingUsage metrics
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