Visual odometry (VO) is a prevalent way to deal with the relative localization problem, which
is becoming increasingly mature and accurate, but it tends to be fragile under challenging …

Explosive growth of big data demands efficient and fast algorithms for nearest neighbor
search. Deep learning-based hashing methods have proved their efficacy to learn advanced …

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 content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

In this paper, we propose a novel benchmark for evaluating local image descriptors. We
demonstrate that the existing datasets and evaluation protocols do not specify …

Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

Nearest neighbor search aims at obtaining the samples in the database with the smallest
distances from them to the queries, which is a basic task in a range of fields, including …

Recent vision and learning studies show that learning compact hash codes can facilitate
massive data processing with significantly reduced storage and computation. Particularly …

In many machine learning applications, labeled data is scarce and obtaining more labels is
expensive. We introduce a new approach to supervising neural networks by specifying …

To convert the input into binary code, hashing algorithm has been widely used for
approximate nearest neighbor search on large-scale image sets due to its computation and …