In the early days, content-based image retrieval (CBIR) was studied with global features.
Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively …

The explosive increase and ubiquitous accessibility of visual data on the Web have led to
the prosperity of research activity in image search or retrieval. With the ignorance of visual …

Abstract Visual Place Recognition is a challenging task for robotics and autonomous
systems, which must deal with the twin problems of appearance and viewpoint change in an …

While image retrieval and instance recognition techniques are progressing rapidly, there is a
need for challenging datasets to accurately measure their performance--while posing novel …

Image descriptors based on activations of Convolutional Neural Networks (CNNs) have
become dominant in image retrieval due to their discriminative power, compactness of …

We seek to predict the 6 degree-of-freedom (6DoF) pose of a query photograph with respect
to a large indoor 3D map. The contributions of this work are three-fold. First, we develop a …

In this paper we address issues with image retrieval benchmarking on standard and popular
Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset, and …

We propose a novel approach for instance-level image retrieval. It produces a global and
compact fixed-length representation for each image by aggregating many region-wise …

Recently, image representation built upon Convolutional Neural Network (CNN) has been
shown to provide effective descriptors for image search, outperforming pre-CNN features as …

While deep learning has become a key ingredient in the top performing methods for many
computer vision tasks, it has failed so far to bring similar improvements to instance-level …