Visual navigation localizes a query place image against a reference database of place images, also known as a `visual map'. Localization accuracy requirements for specific areas of the visual map, `scene classes', vary according to the context of the environment and task. State-of-the-art visual mapping is unable to reflect these requirements by explicitly targetting scene classes for inclusion in the map. Four different scene classes, including pedestrian crossings and stations, are identified in each of the Nordland and St. Lucia datasets. Instead of re-training separate scene classifiers which struggle with these overlapping scene classes we make our first contribution: defining the problem of `scene retrieval'. Scene retrieval extends image retrieval to classification of scenes defined at test time by associating a single query image to reference images of scene classes. Our second contribution is a triplet-trained convolutional neural network (CNN) to address this problem which increases scene classification accuracy by up to 7% against state-of-the-art networks pre-trained for scene recognition. The second contribution is an algorithm `DMC' that combines our scene classification with distance and memorability for visual mapping. Our analysis shows that DMC includes 64% more images of our chosen scene classes in a visual map than just using distance interval mapping. State-of-the-art visual place descriptors AMOS-Net, Hybrid-Net and NetVLAD are finally used to show that DMC improves scene class localization accuracy by a mean of 3% and localization accuracy of the remaining map images by a mean of 10% across both datasets.

翻译：视觉导航根据一个地方图像参考数据库(又称“视觉地图 ” ) 将一个查询位置图像本地化。 视觉地图的具体区域“ 屏幕类” 的本地化准确性要求因环境和任务背景而异。 状态视觉制图无法通过明确定位图像类别来反映这些要求。 在Nordland 和 St. Lucia 的每个数据集中, 都确定了四个不同的场景类别, 包括行人交叉点和站点。 而不是再培训与这些重叠的场景分类进行斗争的单独场景分类, 我们作出的第一个贡献是: 界定“ 屏幕检索” 的问题。 采样检索将图像检索扩展到测试时间界定的场景分类, 将单一查询图像与显示场景类图像的图像联系起来。 我们的三重心神经神经网络( CNN) 解决这个问题, 将现场分类的准确性提高到7%, 用于现场识别之前的状态和状态网络网络网络。 第二项贡献是算法“ DMC ”, 将我们所选的场景场景图像分类与最终的距离和图像分析结合起来, 显示我们所选的图像的图像的图像的距离显示位置分析包括了我们所选的图像的图像的距离和图像的图像的距离, 。