5,555 research outputs found
Technology Integration around the Geographic Information: A State of the Art
One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented
Sketch-based Queries in Mobile GIS-Environments
Recent achievements in the field of mobile computing and wireless communication promise data retrieval anywhere and anytime. This development provided the basis to expand GIs technology to handheld devices, such as personal digital assistants (PDAs). Although traditional GIs technology is well suited for desktop workstations, it needs to be adapted in order to satisfy the requirements of users using handheld computing devices. This adaptation is necessary because the usability of traditional GISs depends on characteristics of desktop computers, such as their relatively large user interfaces (e.g., displays, keyboards, pointing devices), considerable computing resources (i.e., CPU, memory, storage, operating systems), and high bandwidth network connectivity. Small devices possess few of these characteristics, hence, requiring new and efficient methods for interaction with spatial databases. We propose a concept that supports sketch-based querying in mobile GIs environments. This concept combines newest techniques for spatial querying and mobile technologies. Such a combination is beneficial for users because it allows them to formulate queries by drawing the desired configuration with a pen on the touch-sensitive PDA screen, and consequently avoids typing complex statements in some SQL-like query language. Client-server architectures in mobile environments are characterized by low and fluctuating bandwidth, and by frequent disconnections. We discuss client-server strategies in mobile environments, suggest an adaptive client-server architecture for geomobile querying, and analyze the performance. It is shown that adaptation to the mobile environment is necessary in order to ensure efficiency of geo-mobile queries
Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval
Free-hand sketch-based image retrieval (SBIR) is a specific cross-view
retrieval task, in which queries are abstract and ambiguous sketches while the
retrieval database is formed with natural images. Work in this area mainly
focuses on extracting representative and shared features for sketches and
natural images. However, these can neither cope well with the geometric
distortion between sketches and images nor be feasible for large-scale SBIR due
to the heavy continuous-valued distance computation. In this paper, we speed up
SBIR by introducing a novel binary coding method, named \textbf{Deep Sketch
Hashing} (DSH), where a semi-heterogeneous deep architecture is proposed and
incorporated into an end-to-end binary coding framework. Specifically, three
convolutional neural networks are utilized to encode free-hand sketches,
natural images and, especially, the auxiliary sketch-tokens which are adopted
as bridges to mitigate the sketch-image geometric distortion. The learned DSH
codes can effectively capture the cross-view similarities as well as the
intrinsic semantic correlations between different categories. To the best of
our knowledge, DSH is the first hashing work specifically designed for
category-level SBIR with an end-to-end deep architecture. The proposed DSH is
comprehensively evaluated on two large-scale datasets of TU-Berlin Extension
and Sketchy, and the experiments consistently show DSH's superior SBIR
accuracies over several state-of-the-art methods, while achieving significantly
reduced retrieval time and memory footprint.Comment: This paper will appear as a spotlight paper in CVPR201
Multi-modal Embedding Fusion-based Recommender
Recommendation systems have lately been popularized globally, with primary
use cases in online interaction systems, with significant focus on e-commerce
platforms. We have developed a machine learning-based recommendation platform,
which can be easily applied to almost any items and/or actions domain. Contrary
to existing recommendation systems, our platform supports multiple types of
interaction data with multiple modalities of metadata natively. This is
achieved through multi-modal fusion of various data representations. We
deployed the platform into multiple e-commerce stores of different kinds, e.g.
food and beverages, shoes, fashion items, telecom operators. Here, we present
our system, its flexibility and performance. We also show benchmark results on
open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure
SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial Objects
With the proliferation of mobile devices and location-based services,
continuous generation of massive volume of streaming spatial objects (i.e.,
geo-tagged data) opens up new opportunities to address real-world problems by
analyzing them. In this paper, we present a novel continuous bursty region
detection problem that aims to continuously detect a bursty region of a given
size in a specified geographical area from a stream of spatial objects.
Specifically, a bursty region shows maximum spike in the number of spatial
objects in a given time window. The problem is useful in addressing several
real-world challenges such as surge pricing problem in online transportation
and disease outbreak detection. To solve the problem, we propose an exact
solution and two approximate solutions, and the approximation ratio is
in terms of the burst score, where is a parameter
to control the burst score. We further extend these solutions to support
detection of top- bursty regions. Extensive experiments with real-world data
are conducted to demonstrate the efficiency and effectiveness of our solutions
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