DMPCANet: A Low Dimensional Aggregation Network for Visual Place Recognition

Abstract

Visual place recognition (VPR) aims to estimate the geographical location of a query image by finding its nearest reference images from a large geo-tagged database. Most of the existing methods adopt convolutional neural networks to extract feature maps from images. Nevertheless, such feature maps are high-dimensional tensors, and it is a challenge to effectively aggregate them into a compact vector representation for efficient retrieval. To tackle this challenge, we develop an end-to-end convolutional neural network architecture named DMPCANet. The network adopts the regional pooling module to generate feature tensors of the same size from images of different sizes. The core component of our network, the Differentiable Multilinear Principal Component Analysis (DMPCA) module, directly acts on tensor data and utilizes convolution operations to generate projection matrices for dimensionality reduction, thereby reducing the dimensionality to one sixteenth. This module can preserve crucial information while reducing data dimensions. Experiments on two widely used place recognition datasets demonstrate that our proposed DMPCANet can generate low-dimensional discriminative global descriptors and achieve the state-of-the-art results.