کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9669558 | 869093 | 2005 | 23 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
A key problem in learning representations of multiple objects from unlabeled images is that it is a priori impossible to tell which part of the image corresponds to each individual object, and which part is irrelevant clutter. Distinguishing individual objects in a scene would allow unsupervised learning of multiple objects from unlabeled images. There is psychophysical and neurophysiological evidence that the brain employs visual attention to select relevant parts of the image and to serialize the perception of individual objects. We propose a method for the selection of salient regions likely to contain objects, based on bottom-up visual attention. By comparing the performance of David Lowe's recognition algorithm with and without attention, we demonstrate in our experiments that the proposed approach can enable one-shot learning of multiple objects from complex scenes, and that it can strongly improve learning and recognition performance in the presence of large amounts of clutter.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 100, Issues 1â2, OctoberâNovember 2005, Pages 41-63
Journal: Computer Vision and Image Understanding - Volume 100, Issues 1â2, OctoberâNovember 2005, Pages 41-63
نویسندگان
Dirk Walther, Ueli Rutishauser, Christof Koch, Pietro Perona,