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the parameters for OIS/ODS #10
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Like in the gPb, they output a edge probability map, so the threshold for determing whether a pixel is in edge is the parameter would be tuned in generating Pb, Rb and Fb. So in this context, ODS refers to the best performance when selecting optimal the threshold for the whole dataset, and OIS refers to the best performance when selecting the threshold for each image. But in your method, the network should output a deterministic partition for a graph in a specific superpixel merging threshold. So the parameters tuned for ODS and OIS is the superpixel merging threshold? |
您好,十分感谢您之前对我的回复! |
简单来说,就是我对评价分割方法的 Fb 有些疑惑,不知道是调节哪个参数产生的 PR 曲线,是针对不同的方法有不同的参数吗? 以及想问下您, SEISM 中评价分割方法的代码是这个吗? https://github.com/jponttuset/seism/blob/master/src/measures/eval_segm.m |
我这边阅读了 “Learning to Detect Natural Image Boundaries Using Local Brightness, Color,and Texture Cues” 和 “Contour Detection and Hierarchical Image Segmentation”两篇文章,对 P-R curve for boundary 这个指标还是有上面的疑惑 |
调节superpixel merging的参数。除此之外,DEL也没其他参数了啊 |
您好,我对这个 ods、ois 的评估过程还是有些疑惑,我想再请问下,在您的del这个工作中,对 ods 和 ois 这两种情况具体是怎么调节的参数呢, |
请您阅读一下这个代码:https://github.com/yun-liu/DEL/blob/master/examples/del/test_bsds_seg.ipynb 运行这个脚本,会生成不同参数下的分割结果,最后用SEISM的代码评测。 |
Hi, in your paper, you mentioned "Optimal dataset scale (ODS) usually refers to the best performance when selecting optimal parameters for the whole dataset, while optimal image scale (OIS) refers to the best performance when selecting special parameters for each image. "
Are the parameters here refering to the merging threshold or the network parameters?
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