Reconstrução de filogenias para imagens e vídeos

Orientadores: Anderson de Rezende Rocha, Zanoni DiasTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Com o advento das redes sociais, documentos digitais (e.g., imagens e vídeos) se tornaram poderosas ferramentas de comunicação. Dada esta nova realidade, é comum esses documentos serem publicados, compartilhados, modificados e republicados por vários usuários em diferentes canais da Web. Além disso, com a popularização de programas de edição de imagens e vídeos, muitas vezes não somente cópias exatas de documentos estão disponíveis, mas, também, versões modificadas das fontes originais (duplicatas próximas). Entretanto, o compartilhamento de documentos facilita a disseminação de conteúdo abusivo (e.g., pornografia infantil), que não respeitam direitos autorais e, em alguns casos, conteúdo difamatório, afetando negativamente a imagem pública de pessoas ou corporações (e.g., imagens difamatórias de políticos ou celebridades, pessoas em situações constrangedoras, etc.). Muitos pesquisadores têm desenvolvido, com sucesso, abordagens para detecção de duplicatas de documentos com o intuito de identificar cópias semelhantes de um dado documento multimídia (e.g., imagem, vídeo, etc.) publicado na Internet. Entretanto, somente recentemente têm se desenvolvido as primeiras pesquisas para ir além da detecção de duplicatas e encontrar a estrutura de evolução de um conjunto de documentos relacionados e modificados ao longo do tempo. Para isso, é necessário o desenvolvimento de abordagens que calculem a dissimilaridade entre duplicatas e as separem corretamente em estruturas que representem a relação entre elas de forma automática. Este problema é denominado na literatura como Reconstrução de Filogenia de Documentos Multimídia. Pesquisas na área de filogenia de documentos multimídia são importantes para auxiliar na resolução de problemas como, por exemplo, análise forense, recuperação de imagens por conteúdo e rastreamento de conteúdo ilegal. Nesta tese de doutorado, apresentamos abordagens desenvolvidas para solucionar o problema de filogenias para imagens e vídeos digitais. Considerando imagens, propomos novas abordagens para tratar o problema de filogenia considerando dois pontos principais: (i) a reconstrução de florestas, importante em cenários onde se tem um conjunto de imagens semanticamente semelhantes, mas geradas por fontes ou em momentos diferentes no tempo; e (ii) novas medidas para o cálculo de dissimilaridade entre as duplicatas, uma vez que esse cálculo afeta diretamente a qualidade de reconstrução da filogenia. Os resultados obtidos com as soluções para filogenia de imagens apresentadas neste trabalho confirmam a efetividade das abordagens propostas, identificando corretamente as raízes das florestas (imagens originais de uma sequencia de evolução) com até 95% de acurácia. Para filogenia de vídeos, propomos novas abordagens que realizam alinhamento temporal nos vídeos antes de se calcular a dissimilaridade, uma vez que, em cenários reais, os vídeos podem estar desalinhados temporalmente, terem sofrido recorte temporal ou serem comprimidos, por exemplo. Nesse contexto, nossas abordagens conseguem identificar a raiz das árvores com acurácia de até 87%Abstract: Digital documents (e.g., images and videos) have become powerful tools of communication with the advent of social networks. Within this new reality, it is very common these documents to be published, shared, modified and often republished by multiple users on different web channels. Additionally, with the popularization of image editing software and online editor tools, in most of the cases, not only their exact duplicates will be available, but also manipulated versions of the original source (near duplicates). Nevertheless, this document sharing facilitates the spread of abusive content (e.g., child pornography), copyright infringement and, in some cases, defamatory content, adversely affecting the public image of people or corporations (e.g., defamatory images of politicians and celebrities, people in embarrassing situations, etc.). Several researchers have successfully developed approaches for the detection and recognition of near-duplicate documents, aiming at identifying similar copies of a given multimedia document (e.g., image, video, etc.) published on the Internet. Notwithstanding, only recently some researches have developed approaches that go beyond the near-duplicate detection task and aim at finding the ancestral relationship between the near duplicates and the original source of a document. For this, the development of approaches for calculating the dissimilarity between near duplicates and correctly reconstruct structures that represent the relationship between them automatically is required. This problem is referred to in the literature as Multimedia Phylogeny. Solutions for multimedia phylogeny can help researchers to solve problems in forensics, content-based document retrieval and illegal-content document tracking, for instance. In this thesis, we designed and developed approaches to solve the phylogeny reconstruction problem for digital images and videos. Considering images, we proposed approaches to deal with the phylogeny problem considering two main points: (i) the forest reconstruction, an important task when we consider scenarios in which there is a set of semantically similar images, but generated by different sources or at different times; and (ii) new measures for dissimilarity calculation between near-duplicates, given that the dissimilarity calculation directly impacts the quality of the phylogeny reconstruction. The results obtained with our approaches for image phylogeny showed effective, identifying the root of the forests (original images of an evolution sequence) with accuracy up to 95%. For video phylogeny, we developed a new approach for temporal alignment in the video sequences before calculating the dissimilarity between them, once that, in real-world conditions, a pair of videos can be temporally misaligned, one video can have some frames removed and video compression can be applied, for example. For such problem, the proposed methods yield up to 87% correct of accuracy for finding the roots of the treesDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2013/05815-2FAPESPCAPE

Asymmetric visual representation of sex from human body shape

We efficiently infer others' states and traits from their appearance, and these inferences powerfully shape our social behaviour. One key trait is sex, which is strongly cued by the appearance of the body. What are the visual representations that link body shape to sex? Previous studies of visual sex judgment tasks find observers have a bias to report "male", particularly for ambiguous stimuli. This finding implies a representational asymmetry - that for the processes that generate a sex percept, the default output is "male", and "female" is determined by the presence of additional perceptual evidence. That is, female body shapes are positively coded by reference to a male default shape. This perspective makes a novel prediction in line with Treisman's studies of visual search asymmetries: female body targets should be more readily detected amongst male distractors than vice versa. Across 10 experiments (N = 32 each) we confirmed this prediction and ruled out alternative low-level explanations. The asymmetry was found with profile and frontal body silhouettes, frontal photographs, and schematised icons. Low-level confounds were controlled by balancing silhouette images for size and homogeneity, and by matching physical properties of photographs. The female advantage was nulled for inverted icons, but intact for inverted photographs, suggesting reliance on distinct cues to sex for different body depictions. Together, these findings demonstrate a principle of the perceptual coding that links bodily appearance with a significant social trait: the female body shape is coded as an extension of a male default. We conclude by offering a visual experience account of how these asymmetric representations arise in the first place

Facial expression recognition in the wild : from individual to group

The progress in computing technology has increased the demand for smart systems capable of understanding human affect and emotional manifestations. One of the crucial factors in designing systems equipped with such intelligence is to have accurate automatic Facial Expression Recognition (FER) methods. In computer vision, automatic facial expression analysis is an active field of research for over two decades now. However, there are still a lot of questions unanswered. The research presented in this thesis attempts to address some of the key issues of FER in challenging conditions mentioned as follows: 1) creating a facial expressions database representing real-world conditions; 2) devising Head Pose Normalisation (HPN) methods which are independent of facial parts location; 3) creating automatic methods for the analysis of mood of group of people. The central hypothesis of the thesis is that extracting close to real-world data from movies and performing facial expression analysis on movies is a stepping stone in the direction of moving the analysis of faces towards real-world, unconstrained condition. A temporal facial expressions database, Acted Facial Expressions in the Wild (AFEW) is proposed. The database is constructed and labelled using a semi-automatic process based on closed caption subtitle based keyword search. Currently, AFEW is the largest facial expressions database representing challenging conditions available to the research community. For providing a common platform to researchers in order to evaluate and extend their state-of-the-art FER methods, the first Emotion Recognition in the Wild (EmotiW) challenge based on AFEW is proposed. An image-only based facial expressions database Static Facial Expressions In The Wild (SFEW) extracted from AFEW is proposed. Furthermore, the thesis focuses on HPN for real-world images. Earlier methods were based on fiducial points. However, as fiducial points detection is an open problem for real-world images, HPN can be error-prone. A HPN method based on response maps generated from part-detectors is proposed. The proposed shape-constrained method does not require fiducial points and head pose information, which makes it suitable for real-world images. Data from movies and the internet, representing real-world conditions poses another major challenge of the presence of multiple subjects to the research community. This defines another focus of this thesis where a novel approach for modeling the perception of mood of a group of people in an image is presented. A new database is constructed from Flickr based on keywords related to social events. Three models are proposed: averaging based Group Expression Model (GEM), Weighted Group Expression Model (GEM_w) and Augmented Group Expression Model (GEM_LDA). GEM_w is based on social contextual attributes, which are used as weights on each person's contribution towards the overall group's mood. Further, GEM_LDA is based on topic model and feature augmentation. The proposed framework is applied to applications of group candid shot selection and event summarisation. The application of Structural SIMilarity (SSIM) index metric is explored for finding similar facial expressions. The proposed framework is applied to the problem of creating image albums based on facial expressions, finding corresponding expressions for training facial performance transfer algorithms

2D and 3D computer vision analysis of gaze, gender and age

Human-Computer Interaction (HCI) has been an active research area for over four decades. Research studies and commercial designs in this area have been largely facilitated by the visual modality which brings diversified functionality and improved usability to HCI interfaces by employing various computer vision techniques. This thesis explores a number of facial cues, such as gender, age and gaze, by performing 2D and 3D based computer vision analysis. The ultimate aim is to create a natural HCI strategy that can fulfil user expectations, augment user satisfaction and enrich user experience by understanding user characteristics and behaviours. To this end, salient features have been extracted and analysed from 2D and 3D face representations; 3D reconstruction algorithms and their compatible real-world imaging systems have been investigated; case study HCI systems have been designed to demonstrate the reliability, robustness, and applicability of the proposed method.More specifically, an unsupervised approach has been proposed to localise eye centres in images and videos accurately and efficiently. This is achieved by utilisation of two types of geometric features and eye models, complemented by an iris radius constraint and a selective oriented gradient filter specifically tailored to this modular scheme. This approach resolves challenges such as interfering facial edges, undesirable illumination conditions, head poses, and the presence of facial accessories and makeup. Tested on 3 publicly available databases (the BioID database, the GI4E database and the extended Yale Face Database b), and a self-collected database, this method outperforms all the methods in comparison and thus proves to be highly accurate and robust. Based on this approach, a gaze gesture recognition algorithm has been designed to increase the interactivity of HCI systems by encoding eye saccades into a communication channel similar to the role of hand gestures. As well as analysing eye/gaze data that represent user behaviours and reveal user intentions, this thesis also investigates the automatic recognition of user demographics such as gender and age. The Fisher Vector encoding algorithm is employed to construct visual vocabularies as salient features for gender and age classification. Algorithm evaluations on three publicly available databases (the FERET database, the LFW database and the FRCVv2 database) demonstrate the superior performance of the proposed method in both laboratory and unconstrained environments. In order to achieve enhanced robustness, a two-source photometric stereo method has been introduced to recover surface normals such that more invariant 3D facia features become available that can further boost classification accuracy and robustness. A 2D+3D imaging system has been designed for construction of a self-collected dataset including 2D and 3D facial data. Experiments show that utilisation of 3D facial features can increase gender classification rate by up to 6% (based on the self-collected dataset), and can increase age classification rate by up to 12% (based on the Photoface database). Finally, two case study HCI systems, a gaze gesture based map browser and a directed advertising billboard, have been designed by adopting all the proposed algorithms as well as the fully compatible imaging system. Benefits from the proposed algorithms naturally ensure that the case study systems can possess high robustness to head pose variation and illumination variation; and can achieve excellent real-time performance. Overall, the proposed HCI strategy enabled by reliably recognised facial cues can serve to spawn a wide array of innovative systems and to bring HCI to a more natural and intelligent state

An ensemble architecture for forgery detection and localization in digital images

Questa tesi presenta un approccio d'insieme unificato - "ensemble" - per il rilevamento e la localizzazione di contraffazioni in immagini digitali. Il focus della ricerca è su due delle più comuni ma efficaci tecniche di contraffazione: "copy-move" e "splicing". L'architettura proposta combina una serie di metodi di rilevamento e localizzazione di manipolazioni per ottenere prestazioni migliori rispetto a metodi utilizzati in modalità "standalone". I principali contributi di questo lavoro sono elencati di seguito. In primo luogo, nel Capitolo 1 e 2 viene presentata un'ampia rassegna dell'attuale stato dell'arte nel rilevamento di manipolazioni ("forgery"), con particolare attenzione agli approcci basati sul deep learning. Un'importante intuizione che ne deriva è la seguente: questi approcci, sebbene promettenti, non possono essere facilmente confrontati in termini di performance perché tipicamente vengono valutati su dataset personalizzati a causa della mancanza di dati annotati con precisione. Inoltre, spesso questi dati non sono resi disponibili pubblicamente. Abbiamo poi progettato un algoritmo di rilevamento di manipolazioni copy-move basato su "keypoint", descritto nel capitolo 3. Rispetto a esistenti approcci simili, abbiamo aggiunto una fase di clustering basato su densità spaziale per filtrare le corrispondenze rumorose dei keypoint. I risultati hanno dimostrato che questo metodo funziona bene su due dataset di riferimento e supera uno dei metodi più citati in letteratura. Nel Capitolo 4 viene proposta una nuova architettura per predire la direzione della luce 3D in una data immagine. Questo approccio sfrutta l'idea di combinare un metodo "data-driven" con un modello di illuminazione fisica, consentendo così di ottenere prestazioni migliori. Al fine di sopperire al problema della scarsità di dati per l'addestramento di architetture di deep learning altamente parametrizzate, in particolare per il compito di scomposizione intrinseca delle immagini, abbiamo sviluppato due algoritmi di generazione dei dati. Questi sono stati utilizzati per produrre due dataset - uno sintetico e uno di immagini reali - con lo scopo di addestrare e valutare il nostro approccio. Il modello di stima della direzione della luce proposto è stato sfruttato in un nuovo approccio di rilevamento di manipolazioni di tipo splicing, discusso nel Capitolo 5, in cui le incoerenze nella direzione della luce tra le diverse regioni dell'immagine vengono utilizzate per evidenziare potenziali attacchi splicing. L'approccio ensemble proposto è descritto nell'ultimo capitolo. Questo include un modulo "FusionForgery" che combina gli output dei metodi "base" proposti in precedenza e assegna un'etichetta binaria (forged vs. original). Nel caso l'immagine sia identificata come contraffatta, il nostro metodo cerca anche di specializzare ulteriormente la decisione tra attacchi splicing o copy-move. In questo secondo caso, viene eseguito anche un tentativo di ricostruire le regioni "sorgente" utilizzate nell'attacco copy-move. Le prestazioni dell'approccio proposto sono state valutate addestrandolo e testandolo su un dataset sintetico, generato da noi, comprendente sia attacchi copy-move che di tipo splicing. L'approccio ensemble supera tutti i singoli metodi "base" in termini di prestazioni, dimostrando la validità della strategia proposta.This thesis presents a unified ensemble approach for forgery detection and localization in digital images. The focus of the research is on two of the most common but effective forgery techniques: copy-move and splicing. The ensemble architecture combines a set of forgery detection and localization methods in order to achieve improved performance with respect to standalone approaches. The main contributions of this work are listed in the following. First, an extensive review of the current state of the art in forgery detection, with a focus on deep learning-based approaches is presented in Chapter 1 and 2. An important insight that is derived is the following: these approaches, although promising, cannot be easily compared in terms of performance because they are typically evaluated on custom datasets due to the lack of precisely annotated data. Also, they are often not publicly available. We then designed a keypoint-based copy-move detection algorithm, which is described in Chapter 3. Compared to previous existing keypoints-based approaches, we added a density-based clustering step to filter out noisy keypoints matches. This method has been demonstrated to perform well on two benchmark datasets and outperforms one of the most cited state-of-the-art methods. In Chapter 4 a novel architecture is proposed to predict the 3D light direction of the light in a given image. This approach leverages the idea of combining, in a data-driven method, a physical illumination model that allows for improved regression performance. In order to fill in the gap of data scarcity for training highly-parameterized deep learning architectures, especially for the task of intrinsic image decomposition, we developed two data generation algorithms that were used to produce two datasets - one synthetic and one of real images - to train and evaluate our approach. The proposed light direction estimation model has then been employed to design a novel splicing detection approach, discussed in Chapter 5, in which light direction inconsistencies between different regions in the image are used to highlight potential splicing attacks. The proposed ensemble scheme for forgery detection is described in the last chapter. It includes a "FusionForgery" module that combines the outputs of the different previously proposed "base" methods and assigns a binary label (forged vs. pristine) to the input image. In the case of forgery prediction, our method also tries to further specialize the decision between splicing and copy-move attacks. If the image is predicted as copy-moved, an attempt to reconstruct the source regions used in the copy-move attack is also done. The performance of the proposed approach has been assessed by training and testing it on a synthetic dataset, generated by us, comprising both copy-move and splicing attacks. The ensemble approach outperforms all of the individual "base" methods, demonstrating the validity of the proposed strategy

Organising a photograph collection based on human appearance

This thesis describes a complete framework for organising digital photographs in an unsupervised manner, based on the appearance of people captured in the photographs. Organising a collection of photographs manually, especially providing the identities of people captured in photographs, is a time consuming task. Unsupervised grouping of images containing similar persons makes annotating names easier (as a group of images can be named at once) and enables quick search based on query by example. The full process of unsupervised clustering is discussed in this thesis. Methods for locating facial components are discussed and a technique based on colour image segmentation is proposed and tested. Additionally a method based on the Principal Component Analysis template is tested, too. These provide eye locations required for acquiring a normalised facial image. This image is then preprocessed by a histogram equalisation and feathering, and the features of MPEG-7 face recognition descriptor are extracted. A distance measure proposed in the MPEG-7 standard is used as a similarity measure. Three approaches to grouping that use only face recognition features for clustering are analysed. These are modified k-means, single-link and a method based on a nearest neighbour classifier. The nearest neighbour-based technique is chosen for further experiments with fusing information from several sources. These sources are context-based such as events (party, trip, holidays), the ownership of photographs, and content-based such as information about the colour and texture of the bodies of humans appearing in photographs. Two techniques are proposed for fusing event and ownership (user) information with the face recognition features: a Transferable Belief Model (TBM) and three level clustering. The three level clustering is carried out at “event” level, “user” level and “collection” level. The latter technique proves to be most efficient. For combining body information with the face recognition features, three probabilistic fusion methods are tested. These are the average sum, the generalised product and the maximum rule. Combinations are tested within events and within user collections. This work concludes with a brief discussion on extraction of key images for a representation of each cluster

Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã

Towards an efficient, unsupervised and automatic face detection system for unconstrained environments

Nowadays, there is growing interest in face detection applications for unconstrained environments. The increasing need for public security and national security motivated our research on the automatic face detection system. For public security surveillance applications, the face detection system must be able to cope with unconstrained environments, which includes cluttered background and complicated illuminations. Supervised approaches give very good results on constrained environments, but when it comes to unconstrained environments, even obtaining all the training samples needed is sometimes impractical. The limitation of supervised approaches impels us to turn to unsupervised approaches. In this thesis, we present an efficient and unsupervised face detection system, which is feature and configuration based. It combines geometric feature detection and local appearance feature extraction to increase stability and performance of the detection process. It also contains a novel adaptive lighting compensation approach to normalize the complicated illumination in real life environments. We aim to develop a system that has as few assumptions as possible from the very beginning, is robust and exploits accuracy/complexity trade-offs as much as possible. Although our attempt is ambitious for such an ill posed problem-we manage to tackle it in the end with very few assumptions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo