Tree-based indexing for real-time ConvNet landmark-based visual place recognition. (9th January 2017)

• Record Type: Journal Article
• Authors: Hou, Yi; Zhang, Hong; Zhou, Shilin
• Is Part Of: International journal of advanced robotic systems. Volume 14:Number 1(2017:Jan./Feb.)
• Publication Date: 2017-01-09
• Subjects: Visual place recognition -- ConvNet landmark -- tree-based indexing -- k-means tree -- nearest neighbor search; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881416686951
• Languages: English
• ISSNs: 1729-8806

Abstract

Recent impressive studies on using ConvNet landmarks for visual place recognition take an approach that involves three steps: (a) detection of landmarks, (b) description of the landmarks by ConvNet features using a convolutional neural network, and (c) matching of the landmarks in the current view with those in the database views. Such an approach has been shown to achieve the state-of-the-art accuracy even under significant viewpoint and environmental changes. However, the computational burden in step (c) significantly prevents this approach from being applied in practice, due to the complexity of linear search in high-dimensional space of the ConvNet features. In this article, we propose two simple and efficient search methods to tackle this issue. Both methods are built upon tree-based indexing. Given a set of ConvNet features of a query image, the first method directly searches the features' approximate nearest neighbors in a tree structure that is constructed from ConvNet features of database images. The database images are voted on by features in the query image, according to a lookup table which maps each ConvNet feature to its corresponding database image. The database image with the highest vote is considered the solution. Our second method uses a coarse-to-fine procedure: the coarse step uses the first method to coarsely find the top- N database images, and the fine step performs a linear search in Hamming space of the hash codes of the ConvNet features to determine the best match. Experimental results demonstrate that our methods achieve real-time search performance on five data sets with different sizes and various conditions. Most notably, by achieving an average search time of 0.035 seconds/query, our second method improves the matching efficiency by the three orders of magnitude over a linear search baseline on a database with 20, 688 images, with negligible loss in place recognition accuracy.