帕斯卡(单位)
计算机科学
分割
人工智能
图像分割
视觉对象识别的认知神经科学
模式识别(心理学)
计算机视觉
目标检测
背景(考古学)
对象(语法)
生物
古生物学
程序设计语言
作者
Koen E. A. van de Sande,Jasper Uijlings,Theo Gevers,A.W.M. Smeulders
出处
期刊:International Conference on Computer Vision
日期:2011-11-01
被引量:525
标识
DOI:10.1109/iccv.2011.6126456
摘要
For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge.
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