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Journal Article Determining the Existence of Objects in an Image and Its Application to Image Thumbnailing
Cited 7 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Jiwon Choi, Chanho Jung, Jaeho Lee, Changick Kim
Issue Date
2014-08
Citation
IEEE Signal Processing Letters, v.21, no.8, pp.957-961
ISSN
1070-9908
Publisher
IEEE
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.1109/LSP.2014.2321751
Abstract
In recent years, computer vision applications dealing with foreground objects are becoming more important with an increasing demand of advanced intelligent systems. Most of these applications assume that an image contains one or more objects, which often produce undesired results when noticeable objects do not appear in the image. In this letter, we address the problem of ascertaining the existence of objects in an image. In the first step, the input image is partitioned into nonoverlapping local patches, then the patches are categorized into three classes, namely natural, man-made, and object to estimate object candidates. Then a Bayesian methodology is employed to produce more reliable results by eliminating false positives. To boost the object patch detection performance, we exploit the difference between coarse and fine segmentation results. To demonstrate the effectiveness of the proposed method, extensive experiments have been conducted on several benchmark image databases. Furthermore, we have shown the usefulness of our approach by applying it to a real application (i.e., image thumbnailing).
KSP Keywords
Computer Vision(CV), False positive, Foreground objects, Image databases, Intelligent systems, Local patches, Object Candidates, computer vision applications, detection performance, fine segmentation, patch detection