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Type: Conference paper
Title: What's wrong with that object? Identifying images of unusual objects by modelling the detection score distribution
Author: Wang, P.
Liu, L.
Shen, C.
Huang, Z.
Van Den Hengel, A.
Shen, H.
Citation: Proceedings / CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol.2016-December, pp.1573-1581
Publisher: IEEE
Issue Date: 2016
Series/Report no.: IEEE Conference on Computer Vision and Pattern Recognition
ISBN: 9781467388511
ISSN: 1063-6919
Conference Name: 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2016) (26 Jun 2016 - 1 Jul 2016 : Las Vegas, NV)
Statement of
Peng Wang, Lingqiao Liu, Chunhua Shen, Zi Huang, Anton van den Hengel, Heng Tao Shen
Abstract: This paper studies the challenging problem of identifying unusual instances of known objects in images within an "open world" setting. That is, we aim to find objects that are members of a known class, but which are not typical of that class. Thus the "unusual object" should be distinguished from both the "regular object" and the "other objects". Such unusual objects may be of interest in many applications such as surveillance or quality control. We propose to identify unusual objects by inspecting the distribution of object detection scores at multiple image regions. The key observation motivating our approach is that "regular object" images, "unusual object" images and "other objects" images exhibit different region-level scores in terms of both the score values and the spatial distributions. To model these distributions we propose to use Gaussian Processes (GP) to construct two separate generative models, one for the "regular object" and the other for the "other objects". More specifically, we design a new covariance function to simultaneously model the detection score at a single location and the score dependencies between multiple regions. We demonstrate that the proposed approach outperforms comparable methods on a new large dataset constructed for the purpose.
Keywords: Detectors, object recognition, proposals, computer vision, object detection, automobiles, computational modeling
Rights: © 2016 IEEE
DOI: 10.1109/CVPR.2016.174
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