Video Categorization Using Semantics and Semiotics

There is a great need to automatically segment, categorize, and annotate video data, and to develop efficient tools for browsing and searching. We believe that the categorization of videos can be achieved by exploring the concepts and meanings of the videos. This task requires bridging the gap between low-level content and high-level concepts (or semantics). Once a relationship is established between the low-level computable features of the video and its semantics, the user would be able to navigate through videos through the use of concepts and ideas (for example, a user could extract only those scenes in an action film that actually contain fights) rat her than sequentially browsing the whole video. However, this relationship must follow the norms of human perception and abide by the rules that are most often followed by the creators (directors) of these videos. These rules are called film grammar in video production literature. Like any natural language, this grammar has several dialects, but it has been acknowledged to be universal. Therefore, the knowledge of film grammar can be exploited effectively for the understanding of films. To interpret an idea using the grammar, we need to first understand the symbols, as in natural languages, and second, understand the rules of combination of these symbols to represent concepts. In order to develop algorithms that exploit this film grammar, it is necessary to relate the symbols of the grammar to computable video features. In this dissertation, we have identified a set of computable features of videos and have developed methods to estimate them. A computable feature of audio-visual data is defined as any statistic of available data that can be automatically extracted using image/signal processing and computer vision techniques. These features are global in nature and are extracted using whole images, therefore, they do not require any object detection, tracking and classification. These features include video shots, shot length, shot motion content, color distribution, key-lighting, and audio energy. We use these features and exploit the knowledge of ubiquitous film grammar to solve three related problems: segmentation and categorization of talk and game shows; classification of movie genres based on the previews; and segmentation and representation of full-length Hollywood movies and sitcoms. We have developed a method for organizing videos of talk and game shows by automatically separating the program segments from the commercials and then classifying each shot as the host\u27s or guest\u27s shot. In our approach, we rely primarily on information contained in shot transitions and utilize the inherent difference in the scene structure (grammar) of commercials and talk shows. A data structure called a shot connectivity graph is constructed, which links shots over time using temporal proximity and color similarity constraints. Analysis of the shot connectivity graph helps us to separate commercials from program segments. This is done by first detecting stories, and then assigning a weight to each story based on its likelihood of being a commercial or a program segment. We further analyze stories to distinguish shots of the hosts from those of the guests. We have performed extensive experiments on eight full-length talk shows (e.g. Larry King Live, Meet the Press, News Night) and game shows (Who Wants To Be A Millionaire), and have obtained excellent classification with 96% recall and 99% precision. http://www.cs.ucf.edu/~vision/projects/LarryKing/LarryKing.html Secondly, we have developed a novel method for genre classification of films using film previews. In our approach, we classify previews into four broad categories: comedies, action, dramas or horror films. Computable video features are combined in a framework with cinematic principles to provide a mapping to these four high-level semantic classes. We have developed two methods for genre classification; (a) a hierarchical method and (b) an unsupervised classification met hod. In the hierarchical method, we first classify movies into action and non-action categories based on the average shot length and motion content in the previews. Next, non-action movies are sub-classified into comedy, horror or drama categories by examining their lighting key. Finally, action movies are ranked on the basis of number of explosions/gunfire events. In the unsupervised method for classifying movies, a mean shift classifier is used to discover the structure of the mapping between the computable features and each film genre. We have conducted extensive experiments on over a hundred film previews and demonstrated that low-level features can be efficiently utilized for movie classification. We achieved about 87% successful classification. http://www.cs.ucf.edu/-vision/projects/movieClassification/movieClmsification.html Finally, we have addressed the problem of detecting scene boundaries in full-length feature movies. We have developed two novel approaches to automatically find scenes in the videos. Our first approach is a two-pass algorithm. In the first pass, shots are clustered by computing backward shot coherence; a shot color similarity measure that detects potential scene boundaries (PSBs) in the videos. In the second pass we compute scene dynamics for each scene as a function of shot length and the motion content in the potential scenes. In this pass, a scene-merging criterion is used to remove weak PSBs in order to reduce over-segmentation. In our second approach, we cluster shots into scenes by transforming this task into a graph-partitioning problem. This is achieved by constructing a weighted undirected graph called a shot similarity graph (SSG), where each node represents a shot and the edges between the shots are weighted by their similarities (color and motion). The SSG is then split into sub-graphs by applying the normalized cut technique for graph partitioning. The partitions obtained represent individual scenes in the video. We further extend the framework to automatically detect the best representative key frames of identified scenes. With this approach, we are able to obtain a compact representation of huge videos in a small number of key frames. We have performed experiments on five Hollywood films (Terminator II, Top Gun, Gone In 60 Seconds, Golden Eye, and A Beautiful Mind) and one TV sitcom (Seinfeld) that demonstrate the effectiveness of our approach. We achieved about 80% recall and 63% precision in our experiments. http://www.cs.ucf.edu/~vision/projects/sceneSeg/sceneSeg.htm

A Study On Effectiveness Of Movie Trailers Boosting Customers Appreciation Desire: A Customer Science Approach Using Statistics And GSR

In this study, the authors research Effectiveness of Movie Trailers Boosting Customers Appreciation Desire using statistical science and GSR (Galvanic Skin Response) data. As a result of this study, the authors suggest two models of movie trailers boosting customers appreciation desire to make a new movie trailer

Institutional Influence on Documentary Form: an Analysis of PBS and HBO Documentary Programs

INSTITUTIONAL INFLUENCE ON DOCUMENTARY FORM - A COMPARATIVE ANALYSIS OF PBS and HBO DOCUMENTARY PROGRAMS by Mark Irving The University of Wisconsin – Milwaukee, 2015 Under the Supervision of Professor Michael Z. Newman Beginning in the 1980s, the documentary genre has undergone a transformation to accommodate modes of stylistic expression and subjective thematic exposition previously not evident in the genre. This deviation from the form’s traditional modes of expression typically associated with fact-based, journalistic pursuits can be attributed to the institutional underpinnings of media outlets that exhibit documentary programming. These institutional factors, a consequence of an evolving marketplace and shifts in the political and regulatory landscape, have motivated programming mandates or practices often discordant with a media outlet’s stated or presumed mission. This research identifies documentary themes and modes of representation and notes their evolution over time by examining documentary programming on two dominant television networks. I relate these shifts to institutional factors such as fluctuations and changes in funding, administration, regulations and the marketplace - factors such as the decrease in public/tax and consequent rise in private/underwriter funding of public television, and the diversification and increase of programming by commercial media outlets in response to an expanding marketplace. I also draw conclusions about the function of the documentary genre and the nature and purpose of the television institutions that exhibit them - documentary as popular entertainment, journalistic inquiry or historic artifac

The Region in Motion in the Road Movie Patay na Si Hesus (2016)

Utilizing the theories of Bakhtin’s dialogism, Hall’s cultural identity, and Gidden’s globalization, this article analyzes the Cebuano regional road movie Patay na si Hesus (2016). The road film genre reveals that the Philippine regions have a diverse identity, as shown in the image of the regional landscapes and unconventional characters. The use of camera techniques such as the traveling shot and other related styles reveals a dialogue among the diverse cultures of the regions. Furthermore, the image of the automobile in road movies and its mobility also illustrate that the regions combine an image of tradition and modernity as they constantly change because of globalization. Ultimately, this essay affirms that understanding the existence of cultural diversity in the regions is also a means of comprehending their complex identities

The Missouri Miner, January 22, 1976

https://scholarsmine.mst.edu/missouri_miner/3111/thumbnail.jp

Volume 38 - Issue 12 - Friday, January 10, 2003

The Rose Thorn, Rose-Hulman\u27s independent student newspaper.https://scholar.rose-hulman.edu/rosethorn/1289/thumbnail.jp

Spartan Daily, October 2, 1981

Volume 77, Issue 22https://scholarworks.sjsu.edu/spartandaily/6798/thumbnail.jp

Dysphoric Visibility: Discontents of Queer Visibility in the Media

After decades and decades of invisibility and harmful stereotypes, marginalized queers have finally made their way to the media scene, now represented more than ever. Although positive representation deserves recognition, the inclination to celebrate accentuates the urgency to question, and think critically about, what lies beneath this representational surface. Not only are assumptions still being made about queer people by representational media, thus creating new stereotypes and normalizing ‘new’ queer identities, queerness is now more profitable than ever due to its increased societal acceptability. While media representation is important for those who have been systematically erased from visual history, especially in the US where media plays an influential role in everyone’s lives, the cultural idea that civil rights changes can come from inclusion in the consumer sphere often leads to a belief that representational media is a sign of progress. But media that represents queers is not exempt from the system that perpetuates economic inequalities throughout the US and the world, even if it promotes a socially liberal agenda; in fact, representational media successfully absorbs queerness into the capitalist system that many queers continue to be negatively affected by. In this paper I aim to make apparent the unsettling relationship between positive media representations of queer and transgender people, and the capitalist predisposition to make a profit

MoWLD: a robust motion image descriptor for violence detection

© 2015, Springer Science+Business Media New York. Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bag-of-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the non-parametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts