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PCA使用数据源对CDW的影响

实验过程中，发现PCA使用不同数据源对CDW的影响是比较大的。下面是PCA采用不同数据源对CDW的影响实验，实验在oxford上进行，评鉴准则采用mAP。

实验设置：crop、no query expansion、do PCA

数据源	维度	mAP
oxford	512	60.2155%
oxford	256	64.3746%
oxford	128	66.9665%
oxford	64	64.3458%
oxford	32	58.0331%

数据源	维度	mAP
paris	512	70.8359%
paris	256	69.6122%
paris	128	64.0718%
paris	64	58.4009%
paris	32	52.5268%

mAP在小数点后又微小浮动。

no crop对特征的影响

实验设置：no crop、no query expansion, do PCA

数据源	维度	mAP
oxford	512	59.9517%
oxford	256	64.3746%
oxford	128	66.9665%
oxford	64	64.3458%
oxford	32	58.0331%

qe对特征的影响

实验设置：no crop、query expansion、do PCA

top@K	维度	mAP
0	512	59.9517%
1	512	59.9517%
2	512	63.7079%
3	512	65.6768%
4	512	66.7678%
5	512	67.4205%
6	512	68.3001%
7	512	68.9647%
8	512	69.5633%
9	512	69.5831%
10	512	69.8873%

重构代码

重构后的代码完成的功能如下:

• 全图提取特征 or 区域框选提取特征
• do PCA or not
• do query expansion or not
• 特征可视化

重构后的检索精度指标评价：do crop, do qe(top@10), do PCA

维度	mAP
512	71.88%
256	72.04%(73.01%)
128	70.33%
64	65.6768%
32	58.8%

在几百万数据集上获取PCA，然后用在oxford上：

维度	mAP
128	59.15%
可能的原因：Oxford查询图片都是地标图像集，而这几百万数据集都是短视频中的一些数据，导致获得的主成分不利于地标数据的表达，所以精度降低。

HybridNet

do crop, do qe(top@10), do PCA，最高维度256。

维度	mAP
256	62.41%

We chose the HybridNet for several reasons: first, its ar- chitecture is the same as the famous AlexNet [19]; second, the HybridNet has been trained on the ImageNet subset used for ILSVRC competitions (as many others) and the Places Database [29]; last, but not least, experiments conducted on various datasets demonstrate the good transferability of the learning [29, 12, 9]. Originally proposed in [29], Hybrid- Net has been used in [29, 12, 9]. The results reported in [12] show that deep features extracted from the HybridNet outperforms various architectures trained only on ImageNet, on both InriaHolidays and OxforBuilding benchmarks.