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Reference TypeConference Paper (Conference Proceedings)
Last Update2012:
Metadata Last Update2020: administrator
Citation KeySilvaFoSchnOliv:2012:MuSpRe
TitleMulti-Scale Spectral Residual Analysis to Speed up Image Object Detection
FormatDVD, On-line.
Access Date2021, Jan. 27
Number of Files1
Size4541 KiB
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Author1 Silva Filho, José Grimaldo da
2 Schnitman, Leizer
3 Oliveira, Luciano Rebouças de
Affiliation1 Universidade Federal da Bahia
2 Universidade Federal da Bahia
3 Universidade Federal da Bahia
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: -> administrator :: 2012
2020-02-19 02:18:27 :: administrator -> :: 2012
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Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
Keywordsmulti-scale spectral residue, saliency, person detection.
AbstractAccuracy in image object detection has been usually achieved at the expense of much computational load. Therefore a trade-off between detection performance and fast execution commonly represents the ultimate goal of an object detector in real life applications. In this present work, we propose a novel method toward that goal. The proposed method was grounded on a multi-scale spectral residual (MSR) analysis for saliency detection. Compared to a regular sliding window search over the images, in our experiments, MSR was able to reduce by 75% (in average) the number of windows to be evaluated by an object detector. The proposed method was thoroughly evaluated over a subset of LabelMe dataset (person images), improving detection performance in most cases.
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