Commercial detection plays an important role in various video segmentation and indexing applications. It provides high-level program segmentation so that other algorithms can be applied on the true program material in the broadcast. It is a challenge to have robust commercial detection methodology for various platforms, content formats, and broadcast styles that are used all over the world. Wide deployment of such an algorithm not only requires the development of new algorithms but also updating and tuning of parameters for existing algorithms. We present visual commercial detectors that rely on features including, luminance, letterbox, and keyframe distance. These detectors were developed after a careful study of the various features that can be extracted during MPEG-encoding process in real time. Due to the intermittent nature of the features, and platform restrictions, the commercial detection relies on a set of thresholds to keep the implementation as simple as possible. We evolved these thresholds using genetic algorithms (GAs) to optimize the performance. We show how a scalar genetic algorithm can locate sets of parameters in a multi-objective space (precision and recall) that outperform the values selected by an expert engineer. We present the results of optimizing a commercial detection algorithm for different data sets and parameter sets. In this paper we show that GAs drastically improved our approach and enabled fast prototyping and performance tuning of commercial detection algorithms.

Automatic indexing of large collections of multimedia data is important for enabling retrieval functions. Current approaches mostly draw on a single or dual modality of video content analysis. Here we describe a framework for the integration of multimedia content and context information, which generalizes and systematizes current methods. Content information in the visual, audio, and text domains, is described at different levels of granularity and abstraction. Context describes the underlying structural information that can be used to constrain the possible number of interpretations. We introduce a probabilistic framework that combines (a) Bayesian networks that describe both content and context and (b) hierarchical priors that describe the integration of content and context. We present an application that uses this framework to segment and index TV programs. We discuss experimental results on segment classification on six and a half hours of broadcast video. In our experiments we used audio context information. Classification results for financial segments yield 83% and for celebrity segments 89%.

Currently available personal video recorders find and store whole TV programs. Our system, Video Scouting, not only finds and stores programs; it automatically segments and indexes story segments from the programs according to viewers' profiles. The extracted descriptions serve the viewers' content information requests for program segment selection, e.g., play the three minute interview with Hillary Clinton. To achieve this, the system combines information from the audio, visual, and transcript domains in a probabilistic framework based on Bayesian networks. We describe the overall architecture, a system implementation, and discuss some experimental results.

We describe integrated multimedia processing for Video Scout, a system that segments and indexes TV programs according to their audio, visual, and transcript information. Video Scout represents a future direction for personal video recorders. In addition to using electronic program guide metadata and a user profile, Scout allows the users to request specific topics within a program. For example, users can request the video clip of the USA president speaking from a half-hour news program. Video Scout has three modules: (i) video pre-processing, (ii) segmentation and indexing, and (iii) storage and user interface. Segmentation and indexing, the core of the system, incorporates a Bayesian framework that integrates information from the audio, visual, and transcript (closed captions) domains. This framework uses three layers to process low, mid, and high-level multimedia information. The high-level layer generates semantic information about TV program topics. This paper describes the elements of the system and presents results from running Video Scout on real TV programs.

This paper describes the transcript triggers system and method that automatically retrieves information supplemental to video programming. The generated video transcript is searched for the items of interest and related information is returned. The system performs extraction of transcript, and matching of keywords based on a personal profile, pulling information from additional sources and handling the user interface using a rich set of association rules.

Current personal Vido recorders make it very easy for consumers to record whole TV programs. Our research however, focuses on personalizing TV at a sub-program level. We use a traditional Content-Based Information Retrieval system architecture consisting of archiving and retrieval modules. The archiving module employs a three-layered, multimodal integration framework to segment, analyze, characterize, and classify segments. The retrieval module relies on users personal preferences to deliver both full programs and video segments of interest. We tested retrieval concepts with real users and discovered that they see more value in segmenting non-narrative programs (e.g. news) than narrative programs (e.g. movies). We benchmarked individual algorithms and segment classification for celebrity and financial segments as instances of non-narrative content. For celebrity segments we obtained a total precision of 94.1% and recall of 85.7%, and for financial segments a total precision of 81.1% and a recall of 86.9%.

No abstract available.

Methods for video content analysis are necessary for the growing amount of video information delivered to consumers today. In this paper, we present a system for video content analysis called Vitamin, which provides management of a home video library. The system presents the user with a visual table of contents that provides an overview of the video content and direct access to particular points in the stored video. In this process, we apply a computationally inexpensive, simple, yet powerful mechanism for cut detection and keyframe filtering. Our initial implementation and results show that this system can perform video content extraction in real time on a low-end platform that matches a visual table of contents extracted by an expert.

Article Video keyframe extraction and filtering: a keyframe is not a keyframe to everyone Share on Authors: Nevenka Dimitrova Philips Research, 345 Scarborough Rd., Briarcliff Manor NY Philips Research, 345 Scarborough Rd., Briarcliff Manor NYView Profile , Thomas McGee Philips Research, 345 Scarborough Rd., Briarcliff Manor NY Philips Research, 345 Scarborough Rd., Briarcliff Manor NYView Profile , Herman Elenbaas Philips Research, 345 Scarborough Rd., Briarcliff Manor NY Philips Research, 345 Scarborough Rd., Briarcliff Manor NYView Profile Authors Info & Claims CIKM '97: Proceedings of the sixth international conference on Information and knowledge managementJanuary 1997 Pages 113–120https://doi.org/10.1145/266714.266876Online:01 January 1997Publication History 28citation1,003DownloadsMetricsTotal Citations28Total Downloads1,003Last 12 Months12Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access