## 4 Experiments
### Image Retrieval Experiment  
In this section, we perform experiments on the Oxford Building dataset [41] and Paris Building dataset [42] to validate the performance of our coarse-to-fine image retrieval method. The Oxford Building dataset contains 5063 images with 11 different buildings in Oxford, in which the bounding boxes surrounding the objects is also provided. The Paris building dataset contains 6412 images with 11 different buildings. With respect to the training details, the learning rate is set to 0.001, and weight decay and momentum are set to 0.0005 and 0.9 respectively. In coarse filtering stage, we adopt the conv5 layer of ZF to build the global descriptors of images for searching the top-50 images in the database closest to the query image. After that, the ranked Top-50 images were verified for spatial re-ranking based on local descriptors. In order to get better compact representations for image retrieval, we fine-tune the pre-trained network to return the regressed bounding box coordinates and the class scores for each image, which is conductive to obtain the local features of the interest region. In this way, the feature descriptors exhibit strong discrimination in the environment with a similar layout.  
To quantitatively evaluate the retrieval performance, we compare the proposed methods with several image retrieval works in the literature in terms of the accuracy and time cost. We use the widely-used Mean Average Precision (MAP) to evaluate the retrieval accuracy of image retrieval. To make the comparison fair, we fine tune the network model of each method on the auxiliary dataset  
Figure 7: The plan of the experimental environment.  
and performed query expansion with top-5 images. Table 1 shows the results of image retrieval. As can be seen, in terms of MAP, our result is significantly better than [PERSON] et al. [43] and [PERSON] et al. [44] on the Oxford 5k dataset. This may be the reason that our method simultaneously considers global and local features of the image, which is conductive to extract discriminative descriptors from images with similar layouts and geometric structure. Comparing with the well-developed methods [29; 45], our method also obtains comparable performance on the Oxford 5k dataset and Pair 6k dataset. However, in terms of time consumption, they are both relative expensive. The works [29; 45] require a costly spatial verification in test time, comparing the query feature with each of the features in the dataset. In this way, it will be very time-consuming when the dimension of the query vector is high. By contrast, our method only takes 0.17 s, which is about seventeen times faster than Reference [45] and six times faster than Reference [29]. This is mainly because our method transforms the high dimensional CNN features into a more efficient binary code with almost negligible loss of accuracy.  
Figure 8 shows the top retrieved results of several query examples in the Oxford Building dataset and Paris Building dataset. The first image depicted with a blue contour at each row is the query image. The others are the top retrieved images, while positive and negative images are depicted with green and red contour, respectively. The black number below is the ranking of the image and the red number in brackets is the ranking of the image before spatial reranking. It can be seen that our spatial reranking strategy based on local features can effectively improve the performance of image retrieval, especially for images that contain salient local object.