What makes a good picture?

Trabalho de investigação desenvolvido na Cranfield University. School of EngineeringTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Computational Media Aesthetics for Media Synthesis

Ph.DDOCTOR OF PHILOSOPH

Development of a machine learning-based model to autonomously estimate web page credibility

There is a broad range of information available on the Internet, some of which is considered to be more credible than others. People consider different credibility aspects while evaluating the credibility of a web page, however, many web users find it difficult to determine the credibility of all types of web pages. An autonomous system that can analyze different credibility factors extracted from a web page to estimate the page's credibility could help users to make better decisions about the perceived credibility of the web information. This research investigated the applicability of several machine learning approaches to the evaluation of web page credibility. First, six credibility categories were identified from peer-reviewed literature. Then, their related credibility features were investigated and automatically extracted from the web page content, metadata, or external resources. Three sets of features (i.e., automatically extracted credibility features, bag of words features, and combination of both) were used in classification experiments to compare their impact on the autonomous credibility estimation model performance. The Content Credibility Corpus (C3) dataset was used to develop and test the performance of the model developed in this research. XGBoost achieved the best weighted average F1 score for extracted features. In comparison, the Logistic Regression classifier had the best performance when bag of words features was used, and all features together were used as a feature vector. To begin to explore the legitimacy of this approach, a crowdsourcing task was conducted to evaluate how the output of the proposed model aligns with the credibility ratings given by human annotators. Thirty web pages were selected from the C3 dataset to find out how current users' ratings correlate to the ratings that were used as ground truth to train the model. In addition, 30 new web pages were selected to explore how generalizable the algorithm is for classifying new web pages. Participants were asked to rate the credibility of each web page base on a 5-point Likert scale. Sixty-nine crowd-sourced participants evaluated the credibility of the 60 web pages for a total of 600 ratings (10 per page). Spearman's correlation between average credibility scores given by participants and original scores in the C3 dataset indicates a moderate positive correlation: r = 0.44, p < 0.02. A contingency table was created to compare the predicted scores by the model with the rated scores by participants. Overall, the model achieved an accuracy of 80%, which indicates that the proposed model can generalize for new web pages. The model outlined in this thesis outperformed the previous work by using a promising set of features that some of them were presented in this research for the first time

Sistema automático de predicción estética basado en Computación Evolutiva y Deep Learning

Programa Oficial de Doutoramento en Tecnoloxías da Información e as Comunicacións. 5032V01[Resumen] Actualmente, con el auge de las redes sociales, acostumbramos a tomar decisiones en función del valor estético de las imágenes. En el comercio electrónico, por ejemplo, tomamos decisiones de compra en función de las imágenes del producto. En este contexto, un sistema automático que permita seleccionar y ordenar las imágenes en función de su valor estético puede ser de gran valor. Esta tesis aborda diferentes problemas del campo de la estética computacional y propone nuevas soluciones que son validadas finalmente en un caso práctico real. En primer lugar, se estudian los datasets utilizados en estética computacional y se propone una nueva metodología para la creación de conjuntos de imágenes generalizables que se pueda aplicar en problemas de Machine Learning. A continuación, se expone un nuevo enfoque que utiliza transfer learning con un nuevo algoritmo genético híbrido para la predicción del valor estético en imágenes digitales. Finalmente, se aplica a un caso práctico real tanto la metodología de creación de datasets propuesta, como el modelo híbrido que ofreció mejores resultados en la fase experimental. Los resultados sugieren que utilizar estas herramientas en la vida cotidiana pueden mejorar tanto la experiencia de los usuarios como la productividad de los comercios electrónicos.[Resumo] Actualmente, co auxe das redes sociais, acostumamos a tomar decisións en función do valor estético das imaxes. No comercio electrónico, por exemplo, tomamos decisións de compra en función das imaxes do produto. Neste contexto, un sistema automático que permita seleccionar e ordenar as imaxes en función do seu valor est´etico pode ser de gran valor. Esta tese aborda diferentes problemas do campo da estética computacional e propón novas solucións que son validadas finalmente nun caso práctico real. En primeiro lugar, estúdanse os datasets utilizados en estética computacional e proponse unha nova metodoloxía para a creación de conxuntos de imaxes xeneralizables que se poida aplicar en problemas de Machine Learning. A continuación, exponse un novo enfoque que utiliza transfer learning cun novo algoritmo xenético híbrido para a predición do valor estético en imaxes dixitais. Finalmente, aplícase a un caso práctico real tanto a metodoloxía de creación de datasets proposta, como o modelo híbrido que ofreceu mellores resultados na fase experimental. Os resultados suxiren que utilizar estas ferramentas na vida cotiá poden mellorar tanto a experiencia dos usuarios como a produtividade dos comercios electrónicos.[Abstract] Nowadays, with the rise of social media, we are used to making decisions based on the aesthetic value of images. In e-commerce, for example, we make purchasing decisions based on product images. In this context, an automatic system that allows us to select and sort images according to their aesthetic value can be of great value. This thesis addresses different problems in the field of computational aesthetics and proposes new solutions that are finally validated in a real case study. First, we study the datasets used in computational aesthetics and propose a new methodology for the creation of generalisable image sets that can be applied to Machine Learning problems. Then, a new approach using transfer learning with a new hybrid genetic algorithm for the prediction of aesthetic value in digital images is presented. Finally, both the proposed dataset creation methodology and the hybrid model that gave the best results in the experimental phase are applied to a real case study. The results suggest that using these tools can improve both user experience and e-commerce productivity