# Landmark Generation in Visual Place Recognition Using Multi-Scale Sliding Window for Robotics
## 4 Experiments
### Detailed Analysis of Landmark Scale and Space Distributions
While our MSW and SP-Grid produce uniform spatial distribution of the landmarks for both query and reference images, as illustrated in Figure 11b,c, methods using object proposals as the basis for landmark generation such as Edge Boxes often produce uneven spatial distribution (Figure 11d). Intuitively, in Figure 11d, landmarks in query image are distributed in the left side, whereas their counterparts in reference image account for the right side of the image, which makes the matching difficult and, thus, results in the false matches. In addition, it is observed that much of an image with rich texture contents is not involved in the recognition process, and this could be detrimental to the recognition performance. Figure 10: Results of MSW, scale-filtered Edge Boxes and original Edge Boxes for place recognition on: (**a**) Mapillary Berlin; and (**b**) Freiburg Across Seasons dataset  
Figure 9: Landmark matching success rate with respect to landmark scales based on bi-directional nearest neighbor search between the “day-left” and “night-right” sequences. Although it is generally effective, our method still inevitably produces biased matching results in some cases. Analysis of the experimental results shows that, during object detection and matching, our MSW method commit mistakes due to the following specific reasons:  
On the one hand, in its attempt to involve the entire image in matching, MSW includes texture-poor regions (e.g., sky and road), which can contribute to false positive landmark matches due to their similarity. Edge Boxes can in general avoid these false positives better than MSW, however, at the expense of not involving the entire image. Overall, the benefit of involving the entire image outweighs the cost of false positive mistakes, as we have shown in our experiments of comparing MSW with Edge Boxes. Figure 11: An example illustrating two images captured at the same place and the corresponding landmark distributions generated using different methods. (**a**) The query (left) and reference (right) images. The spatial distribution of landmarks extracted from the two images are obtained by: (**b**) our MSW method; (**c**) SP-Grid; and (**d**) Edge Boxes. Deeper color area indicates more coverage by landmarks. On the other hand, although CNN-based features show increased discriminating power with respect to traditional hand-crafted features, they are far from being perfect and, as a result, inevitably cause false matches (both false positive and false negative). This can be easily seen in all our experiments where the precision-recall curves have much to improve, especially on the challenging datasets, and further improvement of landmark descriptors is definitely an open area of research. Our future work will focus on alleviating the above-mentioned drawbacks and further improving the performance of our MSW method. Besides, we will improve the efficiency of our method through introducing the BoCNF [15], which is an efficient image matching method with Bag of ConvNet features to our system.