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  • Language
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  • Created over 9 years ago
  • Updated almost 7 years ago

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Repository Details

image retrieval

图像检索

这个项目会逐渐合并到cnn-cbir-benchmark

开始整理这两三年自己在image retrieval的一些资料,方便来年的毕业设计。

单词数目为100k统计的各项指标

单词数目 SIFT or rootSIFT 空间校正与否 重排数目 检索精度mAP 查询时间(55张)(s)
100k rootSIFT - 62.46% 5.429707
100k rootSIFT 20 66.42% 20.853832
100k rootSIFT 30 68.25% 21.673585
100k rootSIFT 40 69.27% 23.300404
100k rootSIFT 50 69.83% 23.719468
100k rootSIFT 100 72.48% 24.180888
100k rootSIFT 200 75.56% 31.165427
100k rootSIFT 500 78.85% 46.064313
100k rootSIFT 1000 79.93% 70.192928
100k rootSIFT 2000 80.75% 110.999173
100k rootSIFT 3000 80.92% 145.799017
100k rootSIFT 4000 80.97% 176.786657
100k rootSIFT 5063 80.96% 207.201570

单词数目为500k统计的各项指标

单词数目 SIFT or rootSIFT 空间校正与否 重排数目 检索精度mAP 查询时间(55张)(s)
500k rootSIFT - 74.82% 5.345534
500k rootSIFT 20 77.77% 21.646773
500k rootSIFT 30 79.06% 21.615220
500k rootSIFT 40 79.86% 23.453462
500k rootSIFT 50 80.54% 23.588034
500k rootSIFT 100 82.18% 24.942057
500k rootSIFT 200 83.35% 30.585792
500k rootSIFT 500 84.89% 41.023239
500k rootSIFT 1000 85.52% 54.836481
500k rootSIFT 2000 85.73% 67.173112
500k rootSIFT 3000 85.77% 80.634803
500k rootSIFT 5063 85.76% 103.606303

单词数目为1m统计的各项指标

单词数目 SIFT or rootSIFT 空间校正与否 重排数目 检索精度mAP 查询时间(55张)(s)
1m rootSIFT - 77.64% 5.513093
1m rootSIFT 20 80.00% 18.864077
1m rootSIFT 30 80.81% 18.948402
1m rootSIFT 40 81.44% 21.543470
1m rootSIFT 50 82.02% 23.290658
1m rootSIFT 100 83.32% 25.396074
1m rootSIFT 200 84.47% 31.414361
1m rootSIFT 500 85.25% 39.314887
1m rootSIFT 1000 85.51% 46.913126
1m rootSIFT 2000 85.55% 58.102913
1m rootSIFT 3000 85.55% 68.756579
1m rootSIFT 4000 85.55% 77.051332
1m rootSIFT 5063 85.55% 85.428169

查询时间:查询时间是单词查询的结果,并没有进行多次查询进行平均,此外查询时间是查询和计算mAP时间的总和。

MSER

MSER得到椭圆区域后,再结合SIFT,可以剔除掉很多没用的点,VLFeat中的MESR例子见这里。此外MSER还可以用于文本区域筛选中,具体可以看这个Robust Text Detection in Natural Scenes and Web Images。概念与作用相关词:漫水填充法、显著性。

基于SIFT特征点匹配

SIFT on GPU (SiftGPU), works for nVidia, ATI and Intel cards.

Fisher Vector

单词数目 128 to 64 检索精度mAP
256 42.70%
512 52.27%
1024 56.26%
2048 58.68%
4096 62.37%
8192 65.43%
10000 66.88%
20000 69.61%
  • Fisher Vector 512个单词,128降维到64,oxford buiding上mAP为52.27%;L2归一化中如果不采用max的方式,mAP为43.43%。
  • Fisher Vector 1024个单词,128降维到64,oxford buiding上mAP为56.26%;L2归一化中如果不采用max的方式,mAP为47.06%。

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