基于多层次特征差异图的视觉场景识别

场景外观剧烈变化引起的感知偏差和感知变异给视觉场景识别带来了很大的挑战。现有的利用卷积神经网络（CNN）的视觉场景识别方法大多数直接采用CNN特征的距离并设置阈值来衡量两幅图像之间的相似性，当场景外观剧烈变化时效果较差，为此提出了一种新的基于多层次特征差异图的视觉场景识别方法。首先，一个在场景侧重的数据集上预训练的CNN模型被用来对同一场景中感知变异的图像和不同场景中感知偏差的图像进行特征提取。然后，根据CNN不同层特征具有的不同特性，融合多层CNN特征构建多层次特征差异图来表征两幅图像之间的差异。最后，视觉场景识别被看作二分类问题，利用特征差异图训练一个新的CNN分类模型来判断两幅图像是否来自同一场景。实验结果表明，由多层CNN特征构建的特征差异图能很好地反映两幅图像之间的差异，文中提出的方法能有效地克服感知偏差和感知变异，在场景外观剧烈变化下取得很好的识别效果。

Visual place recognition based on multi-level feature difference map

Perceptual aliasing and perceptual variability caused by drastically appearance changing in the scene bring great challenge to visual place recognition. Many existing visual place recognition methods using CNN directly adopted the distance of the CNN features and set thresholds to measure the similarity between the two images, which had shown a poor performance when drastically appearance changing in the scene. A novel multi-level feature difference map based visual place recognition method was proposed. Firstly, a CNN pretrained on scene-centric dataset was adopted to extract features for perceptually different images of same place and aliased images of different places. Then, according to the different properties of different CNN layers, multi-level feature difference map was constructed on the multi-level CNN features to represent the difference between the two images. Finally, visual place recognition was regarded as a binary classification task. The feature difference maps were used to train a new CNN classification model for determining whether the two images are from the same place. Experimental results demonstrated that the feature difference map constructed by multi-level CNN features can well represent the difference between two images, and the proposed method can effectively overcome perceptual aliasing and perceptual variability, and achieve a better recognition performance when drastically appearance changing in the scene.