Convolutional neural networks (CNN) are widely used in computer vision and medical
image analysis as the state-of-the-art technique. In CNN, pooling layers are included mainly …

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 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 …

Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven
notoriously difficult to understand how they reach their decisions. We here introduce a high …

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

Visual place recognition is a challenging task for applications such as autonomous driving
navigation and mobile robot localization. Distracting elements presenting in complex scenes …

In recent years visual place recognition (VPR), ie, the problem of recognizing the location of
images, has received considerable attention from multiple research communities, spanning …

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 …

In recent years a vast amount of visual content has been generated and shared from many
fields, such as social media platforms, medical imaging, and robotics. This abundance of …

We tackle the problem of large scale visual place recognition, where the task is to quickly
and accurately recognize the location of a given query photograph. We present the following …