7,360 research outputs found
Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version
We are witnessing increasing interests in the effective use of road networks.
For example, to enable effective vehicle routing, weighted-graph models of
transportation networks are used, where the weight of an edge captures some
cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions
or travel time. It is a precondition to using a graph model for routing that
all edges have weights. Weights that capture travel times and GHG emissions can
be extracted from GPS trajectory data collected from the network. However, GPS
trajectory data typically lack the coverage needed to assign weights to all
edges. This paper formulates and addresses the problem of annotating all edges
in a road network with travel cost based weights from a set of trips in the
network that cover only a small fraction of the edges, each with an associated
ground-truth travel cost. A general framework is proposed to solve the problem.
Specifically, the problem is modeled as a regression problem and solved by
minimizing a judiciously designed objective function that takes into account
the topology of the road network. In particular, the use of weighted PageRank
values of edges is explored for assigning appropriate weights to all edges, and
the property of directional adjacency of edges is also taken into account to
assign weights. Empirical studies with weights capturing travel time and GHG
emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark)
offer insight into the design properties of the proposed techniques and offer
evidence that the techniques are effective.Comment: This is an extended version of "Using Incomplete Information for
Complete Weight Annotation of Road Networks," which is accepted for
publication in IEEE TKD
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm users’ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to ‘unannotated’ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ‘non-informative
visual words’ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
DEEP FULLY RESIDUAL CONVOLUTIONAL NEURAL NETWORK FOR SEMANTIC IMAGE SEGMENTATION
Department of Computer Science and EngineeringThe goal of semantic image segmentation is to partition the pixels of an image into semantically meaningful parts and classifying those parts according to a predefined label set. Although object recognition
models achieved remarkable performance recently and they even surpass human???s ability to recognize
objects, but semantic segmentation models are still behind. One of the reason that makes semantic
segmentation relatively a hard problem is the image understanding at pixel level by considering global
context as oppose to object recognition. One other challenge is transferring the knowledge of an object
recognition model for the task of semantic segmentation. In this thesis, we are delineating some of the
main challenges we faced approaching semantic image segmentation with machine learning algorithms.
Our main focus was how we can use deep learning algorithms for this task since they require the
least amount of feature engineering and also it was shown that such models can be applied to large scale
datasets and exhibit remarkable performance. More precisely, we worked on a variation of convolutional
neural networks (CNN) suitable for the semantic segmentation task. We proposed a model called deep
fully residual convolutional networks (DFRCN) to tackle this problem. Utilizing residual learning makes
training of deep models feasible which ultimately leads to having a rich powerful visual representation.
Our model also benefits from skip-connections which ease the propagation of information from the
encoder module to the decoder module. This would enable our model to have less parameters in the
decoder module while it also achieves better performance. We also benchmarked the effective variation
of the proposed model on a semantic segmentation benchmark.
We first make a thorough review of current high-performance models and the problems one might
face when trying to replicate such models which mainly arose from the lack of sufficient provided
information. Then, we describe our own novel method which we called deep fully residual convolutional
network (DFRCN). We showed that our method exhibits state of the art performance on a challenging
benchmark for aerial image segmentation.clos
Deteção de veículos e edifícios em imagens aéreas obtidas por drone
The need to develop software for aerial image analysis, captured by Unmanned
Aerial Vehicles, has increased over the years because their use has become more
prevalent in different day-to-day scenarios. Object detection, a Computer Vision technique,
is one of the most explored problems in this area and consists of identifying
and locating objects in images or videos, with the help of Artificial Intelligence technologies.
The aim of this dissertation is to analyze the performance of Deep Learning algorithms
for detecting vehicles and buildings in aerial images. Two of the main
algorithms described in literature, Faster R-CNN and YOLO, the latter in the third
and fifth versions, were chosen to verify which one is capable of better performance.
The dataset provided by the Portuguese Military Academy, which was annotated
and pre-processed, was used for the training of each algorithm and the performance
of tests.
The results obtained in the abovementioned dataset demonstrate that there is a
considerable discrepancy between the two algorithms, both in terms of performance
and speed. Faster R-CNN only proved to be superior to the two versions of YOLO
in terms of training speed, as it was the algorithm that required less time for
training. Among the versions of YOLO, the fifth version showed the best results.A necessidade de desenvolver software para a análise de imagem aérea, capturada
por Veículos Aéreos Não Tripulados, tem vindo a aumentar ao longo dos anos devido
ao facto de serem cada vez mais utilizadas em diversos cenários do dia-a-dia.
A deteção de objetos, técnica da Visão Computacional, é um dos problemas mais
explorados nesta área e consiste na identificação e localização de objetos em imagens
ou vídeos, com o auxílio de tecnologias de Inteligência Artificial.
Pretende-se com esta dissertação analisar o desempenho de algoritmos de Aprendizagem
Profunda, para a deteção de veículos e edifícios em imagens aéreas. Foram
escolhidos dois dos principais algoritmos descritos na literatura, Faster R-CNN e
YOLO, este último na terceira e quinta versão, por forma a verificar qual apresenta
melhor desempenho. Para o treino de cada algoritmo e realização de testes foi utilizado
um conjunto de dados fornecido pela Academia Militar Portuguesa, o qual
foi anotado e pré-processado.
Os resultados obtidos, no referido conjunto de dados, demonstraram que existe
uma discrepância considerável entre os dois algoritmos, tanto a nível do desempenho
como do tempo de deteção. O Faster R-CNN apenas se mostrou superior
em relação às duas versões do YOLO no tempo de treino, pois foi o algoritmo que
precisou de menos tempo. Entre as versões do YOLO, a quinta versão foi a que
apresentou melhores resultados.Mestrado em Engenharia de Computadores e Telemátic
Learning Contextualized Semantics from Co-occurring Terms via a Siamese Architecture
One of the biggest challenges in Multimedia information retrieval and
understanding is to bridge the semantic gap by properly modeling concept
semantics in context. The presence of out of vocabulary (OOV) concepts
exacerbates this difficulty. To address the semantic gap issues, we formulate a
problem on learning contextualized semantics from descriptive terms and propose
a novel Siamese architecture to model the contextualized semantics from
descriptive terms. By means of pattern aggregation and probabilistic topic
models, our Siamese architecture captures contextualized semantics from the
co-occurring descriptive terms via unsupervised learning, which leads to a
concept embedding space of the terms in context. Furthermore, the co-occurring
OOV concepts can be easily represented in the learnt concept embedding space.
The main properties of the concept embedding space are demonstrated via
visualization. Using various settings in semantic priming, we have carried out
a thorough evaluation by comparing our approach to a number of state-of-the-art
methods on six annotation corpora in different domains, i.e., MagTag5K, CAL500
and Million Song Dataset in the music domain as well as Corel5K, LabelMe and
SUNDatabase in the image domain. Experimental results on semantic priming
suggest that our approach outperforms those state-of-the-art methods
considerably in various aspects
'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems
An examination of object recognition challenge leaderboards (ILSVRC,
PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small
differences amongst themselves in terms of error rate/mAP. To better
differentiate the top performers, additional criteria are required. Moreover,
the (test) images, on which the performance scores are based, predominantly
contain fully visible objects. Therefore, `harder' test images, mimicking the
challenging conditions (e.g. occlusion) in which humans routinely recognize
objects, need to be utilized for benchmarking. To address the concerns
mentioned above, we make two contributions. First, we systematically vary the
level of local object-part content, global detail and spatial context in images
from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12.
Second, we propose an object-part based benchmarking procedure which quantifies
classifiers' robustness to a range of visibility and contextual settings. The
benchmarking procedure relies on a semantic similarity measure that naturally
addresses potential semantic granularity differences between the category
labels in training and test datasets, thus eliminating manual mapping. We use
our procedure on the PPSS-12 dataset to benchmark top-performing classifiers
trained on the ILSVRC-2012 dataset. Our results show that the proposed
benchmarking procedure enables additional differentiation among
state-of-the-art object classifiers in terms of their ability to handle missing
content and insufficient object detail. Given this capability for additional
differentiation, our approach can potentially supplement existing benchmarking
procedures used in object recognition challenge leaderboards.Comment: Extended version of our ACCV-2016 paper. Author formatting modifie
Features for Killer Apps from a Semantic Web Perspective
There are certain features that that distinguish killer apps from other ordinary applications. This chapter examines those features in the context of the semantic web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing semantic web applications. Killer apps are highly tranformative technologies that create new e-commerce venues and widespread patterns of behaviour. Information technology, generally, and the Web, in particular, have benefited from killer apps to create new networks of users and increase its value. The semantic web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. The authors hope that this chapter will help to highlight some of the common ingredients of killer apps in e-commerce, and discuss how such applications might emerge in the semantic web
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