3,683 research outputs found
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
Object Level Deep Feature Pooling for Compact Image Representation
Convolutional Neural Network (CNN) features have been successfully employed
in recent works as an image descriptor for various vision tasks. But the
inability of the deep CNN features to exhibit invariance to geometric
transformations and object compositions poses a great challenge for image
search. In this work, we demonstrate the effectiveness of the objectness prior
over the deep CNN features of image regions for obtaining an invariant image
representation. The proposed approach represents the image as a vector of
pooled CNN features describing the underlying objects. This representation
provides robustness to spatial layout of the objects in the scene and achieves
invariance to general geometric transformations, such as translation, rotation
and scaling. The proposed approach also leads to a compact representation of
the scene, making each image occupy a smaller memory footprint. Experiments
show that the proposed representation achieves state of the art retrieval
results on a set of challenging benchmark image datasets, while maintaining a
compact representation.Comment: Deep Vision 201
Acceleration of Computational Geometry Algorithms for High Performance Computing Based Geo-Spatial Big Data Analysis
Geo-Spatial computing and data analysis is the branch of computer science that deals with real world location-based data. Computational geometry algorithms are algorithms that process geometry/shapes and is one of the pillars of geo-spatial computing. Real world map and location-based data can be huge in size and the data structures used to process them extremely big leading to huge computational costs. Furthermore, Geo-Spatial datasets are growing on all V’s (Volume, Variety, Value, etc.) and are becoming larger and more complex to process in-turn demanding more computational resources. High Performance Computing is a way to breakdown the problem in ways that it can run in parallel on big computers with massive processing power and hence reduce the computing time delivering the same results but much faster.This dissertation explores different techniques to accelerate the processing of computational geometry algorithms and geo-spatial computing like using Many-core Graphics Processing Units (GPU), Multi-core Central Processing Units (CPU), Multi-node setup with Message Passing Interface (MPI), Cache optimizations, Memory and Communication optimizations, load balancing, Algorithmic Modifications, Directive based parallelization with OpenMP or OpenACC and Vectorization with compiler intrinsic (AVX). This dissertation has applied at least one of the mentioned techniques to the following problems. Novel method to parallelize plane sweep based geometric intersection for GPU with directives is presented. Parallelization of plane sweep based Voronoi construction, parallelization of Segment tree construction, Segment tree queries and Segment tree-based operations has been presented. Spatial autocorrelation, computation of getis-ord hotspots are also presented. Acceleration performance and speedup results are presented in each corresponding chapter
Design of Combined Coverage Area Reporting and Geo-casting of Queries for Wireless Sensor Networks
In order to efficiently deal with queries or other location dependent information, it is key that the wireless sensor network informs gateways what geographical area is serviced by which gateway. The gateways are then able to e.g. efficiently route queries which are only valid in particular regions of the deployment. The proposed algorithms combine coverage area reporting and geographical routing of queries which are injected by gateways.\u
Efficient Analysis in Multimedia Databases
The rapid progress of digital technology has led to a situation
where computers have become ubiquitous tools. Now we can find them
in almost every environment, be it industrial or even private. With
ever increasing performance computers assumed more and more vital
tasks in engineering, climate and environmental research, medicine
and the content industry. Previously, these tasks could only be
accomplished by spending enormous amounts of time and money. By
using digital sensor devices, like earth observation satellites,
genome sequencers or video cameras, the amount and complexity of
data with a spatial or temporal relation has gown enormously. This
has led to new challenges for the data analysis and requires the use
of modern multimedia databases.
This thesis aims at developing efficient techniques for the analysis
of complex multimedia objects such as CAD data, time series and
videos. It is assumed that the data is modeled by commonly used
representations. For example CAD data is represented as a set of
voxels, audio and video data is represented as multi-represented,
multi-dimensional time series.
The main part of this thesis focuses on finding efficient methods
for collision queries of complex spatial objects. One way to speed
up those queries is to employ a cost-based decompositioning,
which uses interval groups to approximate a spatial object. For
example, this technique can be used for the Digital Mock-Up (DMU)
process, which helps engineers to ensure short product cycles. This
thesis defines and discusses a new similarity measure for time
series called threshold-similarity. Two time series are
considered similar if they expose a similar behavior regarding the
transgression of a given threshold value. Another part of the thesis
is concerned with the efficient calculation of reverse
k-nearest neighbor (RkNN) queries in general metric spaces
using conservative and progressive approximations. The aim of such
RkNN queries is to determine the impact of single objects on the
whole database. At the end, the thesis deals with video
retrieval and hierarchical genre classification of music
using multiple representations. The practical relevance of the
discussed genre classification approach is highlighted with a
prototype tool that helps the user to organize large music
collections.
Both the efficiency and the effectiveness of the presented
techniques are thoroughly analyzed. The benefits over traditional
approaches are shown by evaluating the new methods on real-world
test datasets
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