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

A window on reality: perceiving edited moving images

Edited moving images entertain, inform, and coerce us throughout our daily lives, yet until recently, the way people perceive movies has received little psychological attention. We review the history of empirical investigations into movie perception and the recent explosion of new research on the subject using methods such as behavioral experiments, functional magnetic resonance imagery (fMRI) eye tracking, and statistical corpus analysis. The Hollywood style of moviemaking, which permeates a wide range of visual media, has evolved formal conventions that are compatible with the natural dynamics of attention and humans’ assumptions about continuity of space, time, and action. Identifying how people overcome the sensory differences between movies and reality provides an insight into how the same cognitive processes are used to perceive continuity in the real world

FIGARO, Hair Detection and Segmentation in the Wild

Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of hair detection without relying on a-priori information related to head shape and location. Without using any human-body part classifier, we first classify image patches into hair vs. non-hair by relying on Histogram of Gradients (HOG) and Linear Ternary Pattern (LTP) texture features in a random forest scheme. Then we obtain results at pixel level by refining classified patches by a graph-based multiple segmentation method. Achieved segmentation accuracy (85%) is comparable to state-of-the-art on less challenging databases

Movies and meaning: from low-level features to mind reading

When dealing with movies, closing the tremendous discontinuity between low-level features and the richness of semantics in the viewers' cognitive processes, requires a variety of approaches and different perspectives. For instance when attempting to relate movie content to users' affective responses, previous work suggests that a direct mapping of audio-visual properties into elicited emotions is difficult, due to the high variability of individual reactions. To reduce the gap between the objective level of features and the subjective sphere of emotions, we exploit the intermediate representation of the connotative properties of movies: the set of shooting and editing conventions that help in transmitting meaning to the audience. One of these stylistic feature, the shot scale, i.e. the distance of the camera from the subject, effectively regulates theory of mind, indicating that increasing spatial proximity to the character triggers higher occurrence of mental state references in viewers' story descriptions. Movies are also becoming an important stimuli employed in neural decoding, an ambitious line of research within contemporary neuroscience aiming at "mindreading". In this field we address the challenge of producing decoding models for the reconstruction of perceptual contents by combining fMRI data and deep features in a hybrid model able to predict specific video object classes

A Dataset for Movie Description

Descriptive video service (DVS) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed DVS, which is temporally aligned to full length HD movies. In addition we also collected the aligned movie scripts which have been used in prior work and compare the two different sources of descriptions. In total the Movie Description dataset contains a parallel corpus of over 54,000 sentences and video snippets from 72 HD movies. We characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing DVS to scripts, we find that DVS is far more visual and describes precisely what is shown rather than what should happen according to the scripts created prior to movie production

Digital tools in media studies: analysis and research. An overview

Digital tools are increasingly used in media studies, opening up new perspectives for research and analysis, while creating new problems at the same time. In this volume, international media scholars and computer scientists present their projects, varying from powerful film-historical databases to automatic video analysis software, discussing their application of digital tools and reporting on their results. This book is the first publication of its kind and a helpful guide to both media scholars and computer scientists who intend to use digital tools in their research, providing information on applications, standards, and problems

Digital Tools in Media Studies

Digital tools are increasingly used in media studies, opening up new perspectives for research and analysis, while creating new problems at the same time. In this volume, international media scholars and computer scientists present their projects, varying from powerful film-historical databases to automatic video analysis software, discussing their application of digital tools and reporting on their results. This book is the first publication of its kind and a helpful guide to both media scholars and computer scientists who intend to use digital tools in their research, providing information on applications, standards, and problems

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

Focal Spot, Spring 1982

https://digitalcommons.wustl.edu/focal_spot_archives/1031/thumbnail.jp