Animated movie genre detection using symbolic fusion of text and image descriptors

International audienceThis paper addresses the automatic movie genre classification in the specific case of animated movies. Two types of information are used. The first one are movie synopsis. For each genre, a symbolic representation of a thematic intensity is extracted from synopsis. Addressed visually, movie content is described with symbolic representations of different mid-level color and activity features. A fusion between the text and image descriptions is performed using a set of symbolic rules conveying human expertise. The approach is tested on a set of 107 animated movies in order to estimate their "drama" character. It is observed that the text-image fusion achieves a precision up to 78% and a recall of 44%

Video genre categorization and representation using audio-visual information

International audienceWe propose an audio-visual approach to video genre classification using content descriptors that exploit audio, color, temporal, and contour information. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At the temporal structure level, we consider action content in relation to human perception. Color perception is quantified using statistics of color distribution, elementary hues, color properties, and relationships between colors. Further, we compute statistics of contour geometry and relationships. The main contribution of our work lies in harnessingn the descriptive power of the combination of these descriptors in genre classification. Validation was carried out on over 91 h of video footage encompassing 7 common video genres, yielding average precision and recall ratios of 87% to 100% and 77% to 100%, respectively, and an overall average correct classification of up to 97%. Also, experimental comparison as part of the MediaEval 2011 benchmarkingn campaign demonstrated the efficiency of the proposed audiovisual descriptors over other existing approaches. Finally, we discuss a 3-D video browsing platform that displays movies using efaturebased coordinates and thus regroups them according to genre

Declarative modeling based on knowledge

Les nouvelles technologies de l'image 3D permettent la création de mondes virtuels et des créatures qui les peuplent avec un tel niveau de détails, que pour les effets spéciaux de cinéma, il est difficile de distinguer les éléments sont générés par ordinateur. Cependant, cette technologie est dans les mains habiles de designers, artistes et programmeurs, pour lesquels il faut des semaines à plusieurs années pour se former aux outils et obtenir ces résultats. La Modélisation Déclarative est une méthode qui permet de créer des modèles en fournissant les propriétés donnant la description des composants du modèle. Appliquée à l’infographie, la modélisation déclarative est utilisée pour générer le monde virtuel, en déterminant le contexte nécessaire à l'animation et à la conception de la scène, en calculant la position de chaque objet relativement aux relations spatiales, et en générant le rendu de la scène, utilisé par une système d'animation et de visualisation. Ce mémoire présente les travaux de recherche consacrés à l'utilisation de la modélisation déclarative pour créer des environnements virtuels, en tirant partie des connaissances sur le contexte de la scène. Les connaissances sont utilisées afin de faciliter la tâche de description, en automatisant ce qui peut être déduit, comme les usages et les fonctionnalités habituelles. Elles sont également fondamentales pour que le résultat produit corresponde le mieux possible à ce qui est attendu par le concepteur à partir de la description fournie. Les connaissances sont enfin nécessaires pour faciliter la transition entre le modèle de données et l'architecture qui aura la charge d'animer et de faire évoluer la scène.Modern technology has allowed the creation and presentation or VirtualWorlds and creatures with such a high level of detail, that when used in movies, sometimes it is difficult to tell which elements arecomputer-generated and which not. Also, video-games had reached a level close to photographicrealism. However, such technology is at the hands of skillful designer, artists, and programmers, for whom ittakes from weeks to years to complete these results.Declarative modeling is a method which allows to create models specifying just a few properties for the model’s components. Applied to VW creation, declarative modeling can be used to construct theVW, establishing the layout for the objects, generating the necessary context to provide animation and scene design, and generate the outputs used by a visualization/animation system.This document present a research devoted to explore the use of declarative modeling to create VirtualEnvironments, using knowledge exploitation to support the process and ease the transition from the data model to an underlaying architecture which take the task of animating and evolving the scene

Video Abstracting at a Semantical Level

One the most common form of a video abstract is the movie trailer. Contemporary movie trailers share a common structure across genres which allows for an automatic generation and also reflects the corresponding moviea s composition. In this thesis a system for the automatic generation of trailers is presented. In addition to action trailers, the system is able to deal with further genres such as Horror and comedy trailers, which were first manually analyzed in order to identify their basic structures. To simplify the modeling of trailers and the abstract generation itself a new video abstracting application was developed. This application is capable of performing all steps of the abstract generation automatically and allows for previews and manual optimizations. Based on this system, new abstracting models for horror and comedy trailers were created and the corresponding trailers have been automatically generated using the new abstracting models. In an evaluation the automatic trailers were compared to the original Trailers and showed a similar structure. However, the automatically generated trailers still do not exhibit the full perfection of the Hollywood originals as they lack intentional storylines across shots

Parametric Rough Sets with Application to Granular Association Rule Mining

Granular association rules reveal patterns hidden in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold-start recommendation, where a customer or a product has just entered the system. An example of such rules might be “40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol.” Mining such rules is a challenging problem due to pattern explosion. In this paper, we build a new type of parametric rough sets on two universes and propose an efficient rule mining algorithm based on the new model. Specifically, the model is deliberately defined such that the parameter corresponds to one threshold of rules. The algorithm benefits from the lower approximation operator in the new model. Experiments on two real-world data sets show that the new algorithm is significantly faster than an existing algorithm, and the performance of recommender systems is stable

Keyframe Selection of Frame Similarity to Generate Scene Segmentation Based on Point Operation

Video segmentation has been done by grouping similar frames according to the threshold. Two-frame similarity calculations have been performed based on several operations on the frame: point operation, spatial operation, geometric operation and arithmatic operation. In this research, similarity calculations have been applied using point operation: frame difference, gamma correction and peak signal to noise ratio. Three-point operation has been performed in accordance with the intensity and pixel frame values. Frame differences have been operated based on the pixel value level. Gamma correction has analyzed pixel values and lighting values. The peak signal to noise ratio (PSNR) has been related to the difference value (noise) between the original frame and the next frame. If the distance difference between the two frames was smaller then the two frames were more similar. If two frames had a higher gamma correction factor, then the correction factor would have an increasingly similar effect on the two frames. If the value of PSNR was greater then the comparison of two frames would be more similar. The combination of the three point operation methods would be able to determine several similar frames incorporated in the same segmen

Strategic Categorization, Category Bundle, and Typecasting: Three Essays on Product Categorization

Categories are social agreements about the meanings of labels applied to products. Categories serve as the basis for market interaction: audiences use categories to make sense of the products offered to them, producers apply product categories in marketing activities to reach their target customers, and market intermediaries refer to prototypical categories in assessing the quality of products. As a widely used sociocognitive concept, categorization has accrued prominence in research and practice, with researchers investigating the social and economic impacts of categorization and practitioners probing superior categorization strategies that optimize their economic returns. However, current strategic management and organization theory research has achieved limited success in expounding on how organizations strategically manipulate category labels to acquire excess returns and how audiences process categorical information in assessing the products to which they are exposed. This dissertation joins the ongoing dialogue on categorization and contributes to the literature by offering three essays that respectively address three understudied questions. First, how do producers manipulate the categorical perception of the audiences for their offerings? Second, how do audiences handle the interconnected relationships between categories when they classify products in the market? Last, how do the market identities imposed on market candidates persistently affect their career development? I chose the feature film industry in North America (Canada and the U.S.) as the empirical setting for my dissertation, since a dominant category system, film genres, significantly affects the market success of all film market participants. The genre labels associated with a film shape moviegoers consumption decisions, and the categorical perception of moviegoers of an actor/actress has considerable impacts on the actors/actresss career advancement. Using a gigantic database of feature film projects that were exhibited in theaters in the U.S. and Canada from 1990 to 2015, I construct three unique datasets that are respectively used to test my hypotheses and answer my research questions at the film, genre, and actor levels. I summarize my key findings as follows. This dissertation contributes to category, labor market, and strategic management research

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing