1,294 research outputs found
A bag of words description scheme for image quality assessment
Every day millions of images are obtained, processed, compressed, saved, transmitted and reproduced.
All these operations can cause distortions that affect their quality. The quality of
these images should be measured subjectively. However, that brings the disadvantage of achieving
a considerable number of tests with individuals requested to provide a statistical analysis of
an image’s perceptual quality. Several objective metrics have been developed, that try to model
the human perception of quality. However, in most applications the representation of human
quality perception given by these metrics is far from the desired representation. Therefore,
this work proposes the usage of machine learning models that allow for a better approximation.
In this work, definitions for image and quality are given and some of the difficulties of the study
of image quality are mentioned. Moreover, three metrics are initially explained. One uses the
image’s original quality has a reference (SSIM) while the other two are no reference (BRISQUE
and QAC). A comparison is made, showing a large discrepancy of values between the two kinds
of metrics.
The database that is used for the tests is TID2013. This database was chosen due to its dimension
and by the fact of considering a large number of distortions. A study of each type of distortion
in this database is made.
Furthermore, some concepts of machine learning are introduced along with algorithms relevant
in the context of this dissertation, notably, K-means, KNN and SVM. Description aggregator
algorithms like “bag of words” and “fisher-vectors” are also mentioned.
This dissertation studies a new model that combines machine learning and a quality metric for
quality estimation. This model is based on the division of images in cells, where a specific
metric is computed. With this division, it is possible to obtain local quality descriptors that will
be aggregated using “bag of words”. A SVM with an RBF kernel is trained and tested on the same
database and the results of the model are evaluated using cross-validation.
The results are analysed using Pearson, Spearman and Kendall correlations and the RMSE to
evaluate the representation of the model when compared with the subjective results. The
model improves the results of the metric that was used and shows a new path to apply machine
learning for quality evaluation.No nosso dia-a-dia as imagens sĂŁo obtidas, processadas, comprimidas, guardadas, transmitidas
e reproduzidas. Em qualquer destas operações podem ocorrer distorções que prejudicam a sua
qualidade. A qualidade destas imagens pode ser medida de forma subjectiva, o que tem a
desvantagem de serem necessários vários testes, a um nĂşmero considerável de indivĂduos para
ser feita uma análise estatĂstica da qualidade perceptual de uma imagem. Foram desenvolvidas
várias métricas objectivas, que de alguma forma tentam modelar a percepção humana de
qualidade. Todavia, em muitas aplicações a representação de percepção de qualidade humana
dada por estas métricas fica aquém do desejável, razão porque se propõe neste trabalho usar
modelos de reconhecimento de padrões que permitam uma maior aproximação.
Neste trabalho, são dadas definições para imagem e qualidade e algumas das dificuldades do
estudo da qualidade de imagem são referidas. É referida a importância da qualidade de imagem
como ramo de estudo, e são estudadas diversas métricas de qualidade.
São explicadas três métricas, uma delas que usa a qualidade original como referência (SSIM) e
duas métricas sem referência (BRISQUE e QAC). Uma comparação é feita entre elas, mostrando-
– se uma grande discrepância de valores entre os dois tipos de métricas.
Para os testes feitos Ă© usada a base de dados TID2013, que Ă© muitas vezes considerada para
estudos de qualidade de métricas devido à sua dimensão e ao facto de considerar um grande
número de distorções. Neste trabalho também se fez um estudo dos tipos de distorção incluidos
nesta base de dados e como Ă© que eles sĂŁo simulados.
São introduzidos também alguns conceitos teóricos de reconhecimento de padrões e alguns
algoritmos relevantes no contexto da dissertação, são descritos como o K-means, KNN e as
SVMs. Algoritmos de agregação de descritores como o “bag of words” e o “fisher-vectors”
também são referidos.
Esta dissertação adiciona métodos de reconhecimento de padrões a métricas objectivas de qua–
lidade de imagem. Uma nova técnica é proposta, baseada na divisão de imagens em células, nas
quais uma métrica será calculada. Esta divisão permite obter descritores locais de qualidade
que serão agregados usando “bag of words”. Uma SVM com kernel RBF é treinada e testada na
mesma base de dados e os resultados do modelo sĂŁo mostrados usando cross-validation.
Os resultados são analisados usando as correlações de Pearson, Spearman e Kendall e o RMSE
que permitem avaliar a proximidade entre a métrica desenvolvida e os resultados subjectivos.
Este modelo melhora os resultados obtidos com a métrica usada e demonstra uma nova forma
de aplicar modelos de reconhecimento de padrões ao estudo de avaliação de qualidade
The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits
Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)
Social and content hybrid image recommender system for mobile social networks
One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user
Overlapping and Robust Edge-Colored Clustering in Hypergraphs
A recent trend in data mining has explored (hyper)graph clustering algorithms
for data with categorical relationship types. Such algorithms have applications
in the analysis of social, co-authorship, and protein interaction networks, to
name a few. Many such applications naturally have some overlap between
clusters, a nuance which is missing from current combinatorial models.
Additionally, existing models lack a mechanism for handling noise in datasets.
We address these concerns by generalizing Edge-Colored Clustering, a recent
framework for categorical clustering of hypergraphs. Our generalizations allow
for a budgeted number of either (a) overlapping cluster assignments or (b) node
deletions. For each new model we present a greedy algorithm which approximately
minimizes an edge mistake objective, as well as bicriteria approximations where
the second approximation factor is on the budget. Additionally, we address the
parameterized complexity of each problem, providing FPT algorithms and hardness
results
Learning-Based Approaches for Graph Problems: A Survey
Over the years, many graph problems specifically those in NP-complete are
studied by a wide range of researchers. Some famous examples include graph
colouring, travelling salesman problem and subgraph isomorphism. Most of these
problems are typically addressed by exact algorithms, approximate algorithms
and heuristics. There are however some drawback for each of these methods.
Recent studies have employed learning-based frameworks such as machine learning
techniques in solving these problems, given that they are useful in discovering
new patterns in structured data that can be represented using graphs. This
research direction has successfully attracted a considerable amount of
attention. In this survey, we provide a systematic review mainly on classic
graph problems in which learning-based approaches have been proposed in
addressing the problems. We discuss the overview of each framework, and provide
analyses based on the design and performance of the framework. Some potential
research questions are also suggested. Ultimately, this survey gives a clearer
insight and can be used as a stepping stone to the research community in
studying problems in this field.Comment: v1: 41 pages; v2: 40 page
Theatrical Genre Prediction Using Social Network Metrics
With the emergence of digitization, large text corpora are now available online that provide humanities scholars an opportunity to perform literary analysis leveraging the use of computational techniques. This work is focused on applying network theory concepts in the field of literature to explore correlations between the mathematical properties of the social networks of plays and the plays’ dramatic genre, specifically how well social network metrics can identify genre without taking vocabulary into consideration. Almost no work has been done to study the ability of mathematical properties of network graphs to predict literary features. We generated character interaction networks of 36 Shakespeare plays and tried to differentiate plays based on social network features captured by the character network of each play. We were able to successfully predict the genre of Shakespeare’s plays with the help of social network metrics and hence establish that differences of dramatic genre are successfully captured by the local and global social network metrics of the plays. Since the technique is highly extensible, future work can be extended for fast and detailed literary analysis of larger groups of plays, including plays written in different languages as well as plays written by different authors
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