Darin Brezeale

Bio: Darin Brezeale is an academic researcher from University of Texas at Arlington. The author has contributed to research in topics: Closed captioning & Support vector machine. The author has an hindex of 3, co-authored 4 publications receiving 370 citations.

Automatic Video Classification: A Survey of the Literature

Abstract: There is much video available today. To help viewers find video of interest, work has begun on methods of automatic video classification. In this paper, we survey the video classification literature. We find that features are drawn from three modalities - text, audio, and visual - and that a large variety of combinations of features and classification have been explored. We describe the general features chosen and summarize the research in this area. We conclude with ideas for further research.

Using Closed Captions and Visual Features to Classify Movies by Genre

Abstract: We investigate closed captions and discrete cosine transform coefficients individually as features for classifying movies by genre and learning user preferences. Using a support vector machine as the classifier, we find that these features work very well for classification by genre but the results are less satisfactory when learning user preferences.

Learning Video Preferences Using Visual Features and Closed Captions

Abstract: An approach to identifying a viewer's video preferences uses hidden Markov models by combining visual features and closed captions.

Learning video preferences from video content

Abstract: Viewers of video now have more choices than ever. As the number of choices increases, the task of searching through these choices to locate video of interest is becoming more difficult. Current methods for learning a viewer's preferences in order to automate the search process rely either on video having content descriptions or on having been rated by other viewers identified as being similar. However, much video exists that does not meet these requirements. To address this need, we use hidden Markov models to learn the preferences of a viewer by combining visual features and closed captions. Results are provided from some initial experiments using this approach.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

A Survey on Visual Content-Based Video Indexing and Retrieval

Abstract: Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.

Automatic Video Classification: A Survey of the Literature

Abstract: There is much video available today. To help viewers find video of interest, work has begun on methods of automatic video classification. In this paper, we survey the video classification literature. We find that features are drawn from three modalities - text, audio, and visual - and that a large variety of combinations of features and classification have been explored. We describe the general features chosen and summarize the research in this area. We conclude with ideas for further research.

A Review of Anomaly Detection in Automated Surveillance

Abstract: As surveillance becomes ubiquitous, the amount of data to be processed grows along with the demand for manpower to interpret the data. A key goal of surveillance is to detect behaviors that can be considered anomalous. As a result, an extensive body of research in automated surveillance has been developed, often with the goal of automatic detection of anomalies. Research into anomaly detection in automated surveillance covers a wide range of domains, employing a vast array of techniques. This review presents an overview of recent research approaches on the topic of anomaly detection in automated surveillance. The reviewed studies are analyzed across five aspects: surveillance target, anomaly definitions and assumptions, types of sensors used and the feature extraction processes, learning methods, and modeling algorithms.

High-Level Event Recognition in Unconstrained Videos

Abstract: The goal of high-level event recognition is to automatically detect complex high-level events in a given video sequence. This is a difficult task especially when videos are captured under unconstrained conditions by non-professionals. Such videos depicting complex events have limited quality control, and therefore, may include severe camera motion, poor lighting, heavy background clutter, and occlusion. However, due to the fast growing popularity of such videos, especially on the Web, solutions to this problem are in high demands and have attracted great interest from researchers. In this paper, we review current technologies for complex event recognition in unconstrained videos. While the existing solutions vary, we identify common key modules and provide detailed descriptions along with some insights for each of them, including extraction and representation of low-level features across different modalities, classification strategies, fusion techniques, etc. Publicly available benchmark datasets, performance metrics, and related research forums are also described. Finally, we discuss promising directions for future research.