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Objectness proposal is an emerging problem aiming to improve the efficiency of object detection by reducing candidate windows.The problem was analyzed from the perspective of combinatorial geometric,and a method was pro— posed to construct full cover sets which cover all possible object rectangles with a rather smal amount of windows.For images no larger than 512 X 512,supposing all ohject rectangles are not smaller than 16 X 16,nearly 19000 windows are sufficient to make up a full cover set.By exploiting the prior distribution of locations/sizes of object rectangles,this amount can be reduced further in a greedy mode.In order to address the diversity of 1ow-probability samples of different image sets,a hybrid scheme mixing the greedy and random methods which has good generality was presented.The new scheme recals 94.52 object rectangles with 1000 proposal windows,and its DRs on the first ten hot proposal win— dows are 13.99 ~ 4O.29 higher than existing methods in average.

基于几何信息先验分布的似物性推荐方法 (Objectness Proposal Based on Prior Distribution of Geometric Characteristics of Object Regions).