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 …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

The blooming proliferation of aeronautics and astronautics platforms, together with the ever-
increasing remote sensing imaging sensors on these platforms, has led to the formation of …

We introduce a loss for metric learning, which is inspired by the Lowe's matching criterion for
SIFT. We show that the proposed loss, that maximizes the distance between the closest …

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 …

Benchmark datasets are critical for developing, evaluating, and comparing remote sensing
image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in …

Is it possible to determine the location of a photo from just its pixels? While the general
problem seems exceptionally difficult, photos often contain cues such as landmarks, weather …