Aggregated Deep Local Features for Remote Sensing Image Retrieval

Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing Imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal contributio

Dual Embedding Expansion for Vehicle Re-identification

Vehicle re-identification plays a crucial role in the management of transportation infrastructure and traffic flow. However, this is a challenging task due to the large view-point variations in appearance, environmental and instance-related factors. Modern systems deploy CNNs to produce unique representations from the images of each vehicle instance. Most work focuses on leveraging new losses and network architectures to improve the descriptiveness of these representations. In contrast, our work concentrates on re-ranking and embedding expansion techniques. We propose an efficient approach for combining the outputs of multiple models at various scales while exploiting tracklet and neighbor information, called dual embedding expansion (DEx). Additionally, a comparative study of several common image retrieval techniques is presented in the context of vehicle re-ID. Our system yields competitive performance in the 2020 NVIDIA AI City Challenge with promising results. We demonstrate that DEx when combined with other re-ranking techniques, can produce an even larger gain without any additional attribute labels or manual supervision

Cardiac involvement in Erdheim- Chester disease: MRI findings and literature revision

: Erdheim-Chester disease (ECD) is a rare form of non-Langerhans cell histiocytosis, characterized by the involvement of several organs. The lesions may be skeletal or extra-skeletal: in particular, long bones, skin, lungs, and the cardiovascular and the central nervous systems can be affected. In this report, we describe a case of a 34-year-old man, who came to our observation with symptomatic ECD, for a correct assessment of the degree of cardiac involvement through magnetic resonance imaging (MRI)

Cardiac hybrid imaging: novel tracers for novel targets

Non-invasive cardiac imaging has explored enormous advances in the last few decades. In particular, hybrid imaging represents the fusion of information from multiple imaging modalities, allowing to provide a more comprehensive dataset compared to traditional imaging techniques in patients with cardiovascular diseases. The complementary anatomical, functional and molecular information provided by hybrid systems are able to simplify the evaluation procedure of various pathologies in a routine clinical setting. The diagnostic capability of hybrid imaging modalities can be further enhanced by introducing novel and specific imaging biomarkers. The aim of this review is to cover the most recent advancements in radiotracers development for SPECT/CT, PET/CT, and PET/MRI for cardiovascular diseases

Advanced CMR Techniques in Anderson-Fabry Disease: State of the Art

Anderson-Fabry disease (AFD) is a rare multisystem X-linked lysosomal storage disorder caused by α-galactosidase A enzyme deficiency. Long-term cardiac involvement in AFD results in left ventricular hypertrophy and myocardial fibrosis, inducing several complications, mainly arrhythmias, valvular dysfunction, and coronary artery disease. Cardiac magnetic resonance (CMR) represents the predominant noninvasive imaging modality for the assessment of cardiac involvement in the AFD, being able to comprehensively assess cardiac regional anatomy, ventricular function as well as to provide tissue characterization. This review aims to explore the role of the most advanced CMR techniques, such as myocardial strain, T1 and T2 mapping, perfusion and hybrid imaging, as diagnostic and prognostic biomarkers

Multiscale Convolutional Descriptor Aggregation for Visual Place Recognition

Visual place recognition using query and database images from different sources remains a challenging task in computer vision. Our method exploits global descriptors for efficient image matching and local descriptors for geometric verification. We present a novel, multi-scale aggregation method for local convolutional descriptors, using memory vector construction for efficient aggregation. The method enables to find preliminary set of image candidate matches and remove visually similar but erroneous candidates. We deploy the multi-scale aggregation for visual place recognition on 3 large-scale datasets. We obtain a Recall@10 larger than 94% for the Pittsburgh dataset, outperforming other popular convolutional descriptors used in image retrieval and place recognition. Additionally, we provide a comparison for these descriptors on a more challenging dataset containing query and database images obtained from different sources, achieving over 77% Recall@10

Toward Multilabel Image Retrieval for Remote Sensing

The availability of large-scale remote sensing (RS) data facilitates a wide range of applications, such as disaster management and urban planning. An approach for such problems is image retrieval, where, given a query image, the goal is to find the most relevant match from a database. Most RS literature has been focused on single-label retrieval, where we assume an image has a single label. The primary challenge in single-label RS retrieval is that performance in most datasets is saturated, and it has become difficult to compare the performance of different methods. In this work, we extend the major multilabel classification datasets to the multilabel retrieval problem. We also define protocols, provide evaluation metrics, and study the impact of commonly used loss functions and reranking methods for multilabel retrieval. To this end, a novel multilabel loss function and a reranking technique are proposed, which circumvent the challenges present in conventional single-label image retrieval. The developed loss function considers both class and feature similarity. The proposed reranking technique achieves high performance with computation cost that is well-suited for fast online retrieval