As digital media replaces analog, consumers are beginning to discover that their devices no longer interoperate in the way they are accustomed to. For example, they may find it impossible to transfer a DVR-recorded show onto a portable media player from a different manufacturer. Content creators are weary of supporting myriad ecosystems, each with different rules, restrictions, DRM systems, and file formats. To address these issues, IBM has developed the Advanced Secure Content Cluster Technology (ASCCT), which provides the highest levels of DRM protections for content owners, while at the same time being completely invisible to the user, allowing them to freely move, play, and backup content from any and all ASCCT devices within their home network. We describe the plug-and-play automatic construction of an interoperable secure cluster of heterogeneous devices using a minimal message protocol.

The future of multimedia community depends on how the community effectively and efficiently recruits, nurtures and retains young talents. Students tends to decide on their majors based on job opportunities and the main question in every student mind while finishing a degree is "which jobs are out there for me?" In this panel, we have gathered people from both academia and industry to discuss job opportunities and career perceptive. The panel will try to basically answer two main questions: (1) Which are the jobs for the fresh graduates of our community? (2) What are the carrier paths in both academia and industry?

"Networking of Multimedia Women" event is a continuation of an on-going conversation in the multimedia research community and efforts by the ACM SIGMM to engage and promote female researchers in multimedia community, enable networking of junior and senior female researchers, and give insights towards successful professional careers based on examples.

In this paper, we describe SCOTT: a demonstration system that uses the Set Cover Tracing algorithm for determining the source of pirate content. This algorithm is very efficient in dealing with collusion attacks - the performance is close to linear in the number of colluders. However, the algorithm is based on the Set Cover Problem, which is known to be NP hard. SCOTT confirms the assertion in the original paper that a set cover algorithm is efficient in this particular application. The SCOTT system is suitable for use in a commercial application; the most notable of which is tracing the source of pirate Blu-ray movies. (Blu-ray players contain a built-in tracing traitors key assignment.) It also contains a visualization of the tracing process. After each pirate movie, SCOTT displays the universal of all players and its estimate of guilt for each player.

Devising effective Content Protection mechanisms and building satisfactory Digital Rights Management systems have been top priorities for the Publishing and Entertainment Industries in recent years. In this tutorial, we focus on protection tools and standards for entertainment content. We analyze the challenges of content protection systems, ranging from legal aspects and business models to cryptographic techniques. We discuss current technology licensing practices, describe state-of-the-art broadcast encryption mechanisms and forensic approaches, and present content protection standards such as 4C and AACS.

Multimedia content protection is a controversial topic. Content owners want to protect their rights while consumers want flexible usage, privacy and seamless content flow. In this tutorial we cover from cryptography fundamentals, to history, emerging standards, state-of-the-art approaches and live demos.

As the marketplace for digital media increases we witness the rise of new media distribution models where timely delivery, convenience, privacy and personalization are essential features of competitive offerings. Consumers are looking for innovative ways to access content in a service-oriented manner that suits their mobile life style. This paper describes a prototype standard-based system that allows the secure and fast download of content to SD-Card-enabled consumer devices. Content is protected by Content Protection for Recordable Media (CPRM) technology; specifically we use CPRM SD-Video. Our prototype shows Digital Media Terminals (DMT, also denoted kiosks) that enable innovative rental and purchase models. Our solution combines IBM's plug-in web services, Panasonic's leading-edge devices and Porto Media's high-speed data transfer to Secure Digital Memory Flash Cards (SD-Cards).

Compact representations of video data greatly enhances efficient video browsing. Such representations provide the user with information about the content of the particular sequence being examined while preserving the essential message. We propose a method to automatically generate video summaries using transcripts obtained by automatic speech recognition. We divide the full program into segments based on pause detection and derive a score for each segment, based on the frequencies of the words and bigrams it contains. Then, a summary is generated by selecting the segments with the highest score to duration ratios while at the same time maximizing the coverage of the summary over the full program. We developed an experimental design and a user study to judge the quality of the generated video summaries. We compared the informativeness of the proposed algorithm with two other algorithms for three different programs. The results of the user study demonstrate that the proposed algorithm produces more informative summaries than the other two algorithms

Video data is increasingly being used in medical diagnosis. Due to the quality of the video and the complexities of underlying motion captured, it is difficult for an in-experienced physician/radiologist to describe motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this paper, we present a method of capturing video similarity and its use for diagnosis verification during decision support. Specifically, we describe the motion information in videos using average velocity curves. Second-order motion statistics are extracted from average velocity curves and serve as features for computing video similarity. Given a new video sample already labeled with a diagnosis, a neighborhood of similar videos is assembled from the training set and their diagnosis labels are used to verify the diagnosis.

Speech is a tantalizing mode of human communication. On the one hand, humans understand speech with ease and use speech to express complex ideas, information, and knowledge. On the other hand, automatic speech recognition with computers is still very hard, and extracting knowledge from speech is even harder. In this paper we motivate the study of speech as a knowledge resource and briefly survey a family of related applications and systems being developed at IBM Research aimed towards the goal of exploiting speech as a knowledge resource.

This paper addresses the problem of automatic detection of salient video segments for real-world applications such as corporate training based on associated speech transcriptions. We present a novel segmentation algorithm based on automatic speech recognition (ASR) applied to the audio track of the video. Our feature set consists of word n-grams extracted from the imperfect speech transcriptions. We use a two-pass algorithm that combines a boundary-based method with a content-based method. In the first pass, we analyze the temporal distribution and the rate of arrival of features to compute an initial segmentation. In the second pass, we detect changes in content-bearing words by using the content-bearing features as queries in an information retrieval system. The content-based second pass validates the initial segments and merges them as needed. Variations in the structure of the audio/video content, and the accuracy of ASR have an impact on the feasibility of the segmentation task. For realistic data we observe that we can identify content-rich segments of the audio. In the best scenario a high-level table-of-contents is generated and in the worse scenario a single salient segment is identified. We illustrate the algorithm in detail with some examples and validate the data with manual segmentation boundaries.

This paper describes a new algorithm which discovers the hierarchical organization of a document or media presentation. We use latent semantic indexing to describe the semantic content of the signal, and scale-space segmentation to describe its features at many different scales. We present results from a text document and a video transcript.

This paper describes a new unified representation for the information in a video. We reduce the dimensionality of the signal with either a singular-value decomposition (on the semantic and image data) or mel-frequency cepstral coefficients (on the audio data) and then concatenate the vectors to form a multi-dimensional representation of the video. Using scale-space techniques we find large jumps in the video's path, which we call edges. We use these techniques to analyze the temporal properties of the audio and image data in a video. This analysis creates a hierarchical segmentation of the video, or a table-of-contents, from the audio, semantic and image data.

With more and more streaming media servers becoming commonplace, streaming video has now become a popular medium of instruction, advertisement, and entertainment. With such prevalence comes a new challenge to the servers: Can they track browsing behavior of users to determine what interest users? Learning this information is potentially valuable not only for improved customer tracking and context-sensitive e-commerce, but also in the generation of fast previews of videos for easy pre-downloads. In this paper, we present a formal learning mechanism to track video browsing behavior of users. This information is then used to generate fast video previews. Specifically, we model the states a user transitions while browsing through videos to be the hidden states of a Hidden Markov Model. We estimate the parameters of the HMM using maximum likelihood estimation for each sample observation sequence of user interaction with videos. Video previews are then formed from interesting segments of the video automatically inferred from an analysis of the browsing states of viewers. Audio coherence in the previews is maintained by selecting clips spanning complete clauses containing topically significant spoken phrases. The utility of learning video browsing behavior is demonstrated through user studies and experiments.

algorithm for the segmentation of an audio/video source into topically cohesive segments based on automatic speech recognition (ASR) transcriptions is presented. A novel two-pass algorithm is described that combines a boundary-based method with a content-based method. In the first pass, the temporal proximity and the rate of arrival of ngram features is analyzed in order to compute an initial segmentation. In the content- based second pass, changes in content-bearing words are detected by using the ngram features as queries in an information-retrieval system. The second pass validates the initial segments and merges them as needed. Feasibility of the segmentation task can vary enormously depending on the structure of the audio content, and the accuracy of ASR. For real-world corporate training data our method identifies, at worst, a single salient segment of the audio and, at best, a high-level table-of-contents. We illustrate the algorithm in detail with some examples and validate the results with segmentation boundaries generated manually.

The detection and recognition of events is a challenging problem in video databases. It involves cross-linking and combining information available in multiple modalities such as audio, video and associated text metadata. CueVideo is a system designed for the discovery and recognition of specific events called topics of discussion through advanced video summarization and cross-modal indexing. It supports search for relevant video content through several modes of video summarization including storyboards, moving storyboards and time-scale modified audio summarization. It also enables the recognition and indexing of topical events through cross-model search of audio and video content based on text and image queries respectively.

No abstract available.

The role of audio in the context of multimedia applications involving video is becoming increasingly important. Many efforts in this area focus on audio data that contains some built-in semantic information structure such as in broadcast news, or focus on classification of audio that contains a single type of sound such as cleaar speech or clear music only. In the CueVideo system, we detect and classify audio that consists of mixed audio, i.e. combinations of speech and music together with other types of background sounds. Segmentation of mixed audio has applications in detection of story boundaries in video, spoken document retrieval systems, audio retrieval systems etc. We modify and combine audio features known to be effective in distinguishing speech from music, and examine their behavior on mixed audio. Our preliminary experimental results show that we can achieve a classification accuracy of over 80% for such mixed audio. Our study also provides us with several helpful insights related to analyzing mixed audio in the context of real applications.

No abstract available.

Article Key to effective video retrieval: effective cataloging and browsing Share on Authors: Dulce Ponceleon IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Savitha Srinivasan IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Arnon Amir IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Dragutin Petkovic IBM Almaden Research Center, 650 Harry Road, San Jose, CA IBM Almaden Research Center, 650 Harry Road, San Jose, CAView Profile , Dan Diklic School of Engineering, Santa Clara University, 500 El Camino Real, Santa Clara, CA School of Engineering, Santa Clara University, 500 El Camino Real, Santa Clara, CAView Profile Authors Info & Claims MULTIMEDIA '98: Proceedings of the sixth ACM international conference on MultimediaSeptember 1998 Pages 99–107https://doi.org/10.1145/290747.290760Published:01 September 1998 40citation1,156DownloadsMetricsTotal Citations40Total Downloads1,156Last 12 Months13Last 6 weeks1 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

Video and audio conferencing over networks is becoming increasingly popular due to the availability of video and audio I/O as standard equipment on many computer systems. So far, many algorithms have concentrated on playback only capability. This generally results in unacceptable real-time performance with respect to latency and encoder complexity. We describe a software-only system that allows full duplex video communication. For our analysis and implementation we chose a DCT based method that uses motion estimation and is modelled on the CCITT H.261 standard. We discuss the algorithm, followed by an analysis of the computational requirements for each major block. The results presented show the effect of computational simplifications on signal to noise ratio and image quality. We also examine the processing needs for full resolution coding and project when this will become available.

A small collection of successive frames of a video sequence of a talking person defines a subspace whose dimensionality is much less than the size of one frame. Any face image drawn from the video sequence can be associated with a subspace defined by itself and the frames close to it in time. Representing faces in their associated subspaces can reduce the complexity of further processing that is done on the faces. For a slowly varying video sequence, the subspace that a sliding window of frames will define will also be slowly varying. We describe one technique for efficiently tracking slowly varying subspaces. The method builds and updates the subspace of face images using an adaptation of the Gram-Schmidt orthogonalization procedure. We outline techniques for reducing the dimensions of the subspace while keeping the MSE small, and present one measure for dealing with the perceptual quality of the reconstructions. Finally, we consider subspace tracking in the context of video compression. >

The pile is a new element of the desktop user interface metaphor, designed to support the casual organization of documents. An interface design based on the pile concept suggested uses of content awareness for describing, organizing, and filing textual documents. We describe a prototype implementation of these capabilities, and give a detailed example of how they might appear to the user. We believe the system demonstrates how content awareness can be not only used in a computer filing system, but made an integral part of the user's experience.