Motion and emotion : Semantic knowledge for hollywood film indexing

Ph.DDOCTOR OF PHILOSOPH

Classifying Cinematographic Shot Types

3noIn film-making, the distance from the camera to the subject greatly effects the narrative power of a shot. By the alternate use of Long shots, Medium and Close-ups the director is able to provide emphasis on key passages of the filmed scene. In this work we investigate five different inherent characteristics of single shots which contain indirect information about camera distance, without the need to recover the 3D structure of the scene. Specifically, 2D scene geometric composition, frame colour intensity properties, motion distribution, spectral amplitude and shot content are considered for classifying shots into three main categories. In the experimental phase, we demonstrate the validity of the framework and effectiveness of the proposed descriptors by classifying a significant dataset of movie shots using C4.5 Decision Trees and Support Vector Machines. After comparing the performance of the statistical classifiers using the combined descriptor set, we test the ability of each single feature in distinguishing shot types.Published on-line Nov. 2011; Print publication Jan. 2013partially_openpartially_openCanini L.; Benini S.; Leonardi R.Canini, Luca; Benini, Sergio; Leonardi, Riccard

Who is the director of this movie? Automatic style recognition based on shot features

We show how low-level formal features, such as shot duration, meant as length of camera takes, and shot scale, i.e. the distance between the camera and the subject, are distinctive of a director's style in art movies. So far such features were thought of not having enough varieties to become distinctive of an author. However our investigation on the full filmographies of six different authors (Scorsese, Godard, Tarr, Fellini, Antonioni, and Bergman) for a total number of 120 movies analysed second by second, confirms that these shot-related features do not appear as random patterns in movies from the same director. For feature extraction we adopt methods based on both conventional and deep learning techniques. Our findings suggest that feature sequential patterns, i.e. how features evolve in time, are at least as important as the related feature distributions. To the best of our knowledge this is the first study dealing with automatic attribution of movie authorship, which opens up interesting lines of cross-disciplinary research on the impact of style on the aesthetic and emotional effects on the viewers

Looking at instructional animations through the frame of virtual camera

This thesis investigates the virtual camera and the function of camera movements in expository motion graphics for the purpose of instruction. Motion graphic design is a popular video production technique often employed to create instructional animations that present educational content through the persuasive presentation styles of the entertainment media industry. Adopting animation as a learning tool has distinct concerns and challenges when compared to its use in entertainment, and combining cognitive learning and emotive design aspects requires additional design considerations for each design element. The thesis will address how the camera movement-effect in supporting the narrative and aesthetic in instructional animations. It does this by investigating the virtual camera in terms of technical, semiotic and psychological level, culminating in a systematic categorization of functional camera movements on the basis of conceptual framework that describes hybrid integration of physical, cognitive and affective design aspects; and a creative work as a case study in the form of a comprehensive instructional animation that demonstrates practiced camera movements. Due to the correlation of the conceptual framework relied upon by the supplementary work with the techniques of effective instructional video production and conventional entertainment filmmaking, this thesis touches on the relationship between live action and animation in terms of directing and staging, concluding that the virtual camera as a design factor can be useful for supporting a narrative, evoking emotion and directing the audience’s focus while revealing, tracking and emphasizing informatio

Affective Recommendation of Movies Based on Selected Connotative Features

The apparent difficulty in assessing emotions elicited by movies and the undeniable high variability in subjects emotional responses to filmic content have been recently tackled by exploring film connotative properties: the set of shooting and editing conventions that help in transmitting meaning to the audience. Connotation provides an intermediate representation which exploits the objectivity of audiovisual descriptors to predict the subjective emotional reaction of single users. This is done without the need of registering users physiological signals neither by employing other people highly variable emotional rates, but just relying on the inter-subjectivity of connotative concepts and on the knowledge of users reactions to similar stimuli. This work extends previous by extracting audiovisual and film grammar descriptors and, driven by users rates on connotative properties, creates a shared framework where movie scenes are placed, compared and recommended according to connotation. We evaluate the potential of the proposed system by asking users to assess the ability of connotation in suggesting filmic content able to target their affective requests

Clue: Cross-modal Coherence Modeling for Caption Generation

We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image--caption coherence relations, we annotate 10,000 instances from publicly-available image--caption pairs. We introduce a new task for learning inferences in imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of the generated captions with respect to information needs specified via coherence relations.Comment: Accepted as a long paper to ACL 202

FrameNet annotation for multimodal corpora: devising a methodology for the semantic representation of text-image interactions in audiovisual productions

Multimodal analyses have been growing in importance within several approaches to Cognitive Linguistics and applied fields such as Natural Language Understanding. Nonetheless fine-grained semantic representations of multimodal objects are still lacking, especially in terms of integrating areas such as Natural Language Processing and Computer Vision, which are key for the implementation of multimodality in Computational Linguistics. In this dissertation, we propose a methodology for extending FrameNet annotation to the multimodal domain, since FrameNet can provide fine-grained semantic representations, particularly with a database enriched by Qualia and other interframal and intraframal relations, as it is the case of FrameNet Brasil. To make FrameNet Brasil able to conduct multimodal analysis, we outlined the hypothesis that similarly to the way in which words in a sentence evoke frames and organize their elements in the syntactic locality accompanying them, visual elements in video shots may, also, evoke frames and organize their elements on the screen or work complementarily with the frame evocation patterns of the sentences narrated simultaneously to their appearance on screen, providing different profiling and perspective options for meaning construction. The corpus annotated for testing the hypothesis is composed of episodes of a Brazilian TV Travel Series critically acclaimed as an exemplar of good practices in audiovisual composition. The TV genre chosen also configures a novel experimental setting for research on integrated image and text comprehension, since, in this corpus, text is not a direct description of the image sequence but correlates with it indirectly in a myriad of ways. The dissertation also reports on an eye-tracker experiment conducted to validate the approach proposed to a text-oriented annotation. The experiment demonstrated that it is not possible to determine that text impacts gaze directly and was taken as a reinforcement to the approach of valorizing modes combination. Last, we present the Frame2 dataset, the product of the annotation task carried out for the corpus following both the methodology and guidelines proposed. The results achieved demonstrate that, at least for this TV genre but possibly also for others, a fine-grained semantic annotation tackling the diverse correlations that take place in a multimodal setting provides new perspective in multimodal comprehension modeling. Moreover, multimodal annotation also enriches the development of FrameNets, to the extent that correlations found between modalities can attest the modeling choices made by those building frame-based resources.Análises multimodais vêm crescendo em importância em várias abordagens da Linguística Cognitiva e em diversas áreas de aplicação, como o da Compreensão de Linguagem Natural. No entanto, há significativa carência de representações semânticas refinadas de objetos multimodais, especialmente em termos de integração de áreas como Processamento de Linguagem Natural e Visão Computacional, que são fundamentais para a implementação de multimodalidade no campo da Linguística Computacional. Nesta tese, propomos uma metodologia para estender o método de anotação da FrameNet ao domínio multimodal, uma vez que a FrameNet pode fornecer representações semânticas refinadas, particularmente com um banco de dados enriquecido por Qualia e outras relações interframe e intraframe, como é o caso do FrameNet Brasil. Para tornar a FrameNet Brasil capaz de realizar análises multimodais, delineamos a hipótese de que, assim como as palavras em uma frase evocam frames e organizam seus elementos na localidade sintática que os acompanha, os elementos visuais nos planos de vídeo também podem evocar frames e organizar seus elementos na tela ou trabalhar de forma complementar aos padrões de evocação de frames das sentenças narradas simultaneamente ao seu aparecimento na tela, proporcionando diferentes perfis e opções de perspectiva para a construção de sentido. O corpus anotado para testar a hipótese é composto por episódios de um programa televisivo de viagens brasileiro aclamado pela crítica como um exemplo de boas práticas em composição audiovisual. O gênero televisivo escolhido também configura um novo conjunto experimental para a pesquisa em imagem integrada e compreensão textual, uma vez que, neste corpus, o texto não é uma descrição direta da sequência de imagens, mas se correlaciona com ela indiretamente em uma miríade de formas diversa. A Tese também relata um experimento de rastreamento ocular realizado para validar a abordagem proposta para uma anotação orientada por texto. O experimento demonstrou que não é possível determinar que o texto impacta diretamente o direcionamento do olhar e foi tomado como um reforço para a abordagem de valorização da combinação de modos. Por fim, apresentamos o conjunto de dados Frame2, produto da tarefa de anotação realizada para o corpus seguindo a metodologia e as diretrizes propostas. Os resultados obtidos demonstram que, pelo menos para esse gênero de TV, mas possivelmente também para outros, uma anotação semântica refinada que aborde as diversas correlações que ocorrem em um ambiente multimodal oferece uma nova perspectiva na modelagem da compreensão multimodal. Além disso, a anotação multimodal também enriquece o desenvolvimento de FrameNets, na medida em que as correlações encontradas entre as modalidades podem atestar as escolhas de modelagem feitas por aqueles que criam recursos baseados em frames.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Cinematographic Shot Classification with Deep Ensemble Learning

Cinematographic shot classification assigns a category to each shot either on the basis of the field size or on the movement performed by the camera. In this work, we focus on the camera field of view, which is determined by the portion of the subject and of the environment shown in the field of view of the camera. The automation of this task can help freelancers and studios belonging to the visual creative field in their daily activities. In our study, we took into account eight classes of film shots: long shot, medium shot, full figure, american shot, half figure, half torso, close up and extreme close up. The cinematographic shot classification is a complex task, so we combined state-of-the-art techniques to deal with it. Specifically, we finetuned three separated VGG-16 models and combined their predictions in order to obtain better performances by exploiting the stacking learning technique. Experimental results demonstrate the effectiveness of the proposed approach in performing the classification task with good accuracy. Our method was able to achieve 77% accuracy without relying on data augmentation techniques. We also evaluated our approach in terms of f1 score, precision, and recall and we showed confusion matrices to show that most of our misclassified samples belonged to a neighboring class