AT&T Labs Research - Zavesky, Eric

Eric Zavesky joined AT&T Labs Research in October 2009 as a Principle Member of Technical Staff. At AT&T, he has collaborated on several projects to bring alternative query and retrieval representations to multimedia indexing systems including object-based query, biometric representations for personal authentication, and work to incorporate spatio-temporal information into near-duplicate copy detection. His prior work at Columbia University studied semantic visual representations of content and low-latency, high-accuracy interactive search.

Projects
Assistive Technology, At AT&T Labs - Research, we apply our speech, language and media technologies to give people with disabilities more independence, privacy and autonomy.

Connecting Your World, The need to be connected is greater than ever, and AT&T Researchers are creating new ways for people to connect with one another and with their environments, whether it's their home, office, or car.

Content Augmenting Media (CAM), Leverage multimedia metadata to provide live alerts and intelligent content consumption.

Content-Based Copy Detection, Content-based Copy Detection is an enabling technology to discover repeated content and events in a large-scale content database.

Enhanced Indexing and Representation with Vision-Based Biometrics, Leveraging visual biometrics for indexing and representations of content for retrieval and verification.

iMIRACLE - Content Retrieval on Mobile Devices with Speech, iMIRACLE uses large vocabulary speech recognition for content retrieval with metadata words (titles, genre, channels, etc.) and content words that occur in recorded programs.

MIRACLE and the Content Analysis Engine (CAE), The Multimedia Information Retrieval by Content (MIRACLE) project encompasses the technologies for video indexing, analysis, and retrieval with audio, textual, and visual content information.

VidCat - Simplified Personal Photo and Video Managmenet, VidCat permits simplified personal photo and video management (i.e. a Video Catalog) from a webpage or your favorite mobile device.

Video - Content Delivery and Consumption, A background on the delivery and consumption of video and multimedia and references to projects within the AT&T Video and Multimedia Technologies and Services Research Department.

Video - Indexing and Representation (Metadata), Video and multimedia indexing and representations (i.e. metadata), their production, and use. Links to projects within the AT&T Video and Multimedia Technologies and Services Research Department.

Video and Multimedia Technologies Research, The AT&T Video and Multimedia Technologies Research Department strives to acquire multimedia and video for indexing,retrieval,and consumption with textual,semantic,and visual modalities.

Visual Semantics for Intuitive Mid-Level Representations, Represent content with mid-level visual semantics for retrieval, filtering, and tagging.

The definitive version was published in proceedings of TV Content Analysis (CRC Press/Taylor & Francis). , 2012-02-22, http://mklab.iti.gr/tvca/

Over the years the ﬁdelity and quantity of TV content has steadily increased, but consumers are still experiencing considerable diﬃculties in ﬁnding the content matching their personal interests. New mobile and IP consumption environments have emerged with the promise of ubiquitous delivery of desired content, but in many cases, available content descriptions in the form of electronic program guides lack suﬃcient detail and cumbersome human interfaces yield a less than positive user experience. Creating metadata through a detailed manual annotation of TV content is costly and, in many cases, this metadata may be lost in the content life-cycle as assets are repurposed for multiple distribution channels. Content organization can be daunting when considering domains from breaking news contributions, local or government channels, live sports, music videos, documentaries up through dramatic series and feature ﬁlms. As the line between TV content and Internet content continues to blur, more and more long tail content will appear on TV and the ability to be able to automatically generate metadata for it becomes paramount. Research results from several disciplines must be brought together to address the complex challenge of cost eﬀectively augmenting existing content descriptions to facilitate content personalization and adaptation for users given todays range of content consumption contexts.This chapter presents systems architectures for processing large volumes of video eﬃciently, practical, state of the art solutions for TV content analysis and metadata generation, and potential applications that utilize this metadata in eﬀective and enabling ways.

This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE Consumer Communications and Networking Conference. , 2012-01-15

The definitive version was published in Advertising Research Foundation. , 2012-01-15

Combining content analysis of television programs with quantitative audience measurement can provide insights into customer reactions to advertisements and program content. This work introduces a system architecture that incorporates anonymous audience metrics from an operational IPTV environment with metadata from a content-based analysis of recorded programs. Evaluated on a collection of news programs, the system verifies that events derived from the audience metrics data stream correspond to media segmentation boundaries such as commercial breaks and topic changes. An automated system for executing multimodal media segmentation algorithms for commercial break and topic change detection is also discussed. Better understanding of audience reaction can help IPTV service providers plan infrastructure investments and help in managing multimedia content delivery networks.

The definitive version was published in TRECVID Workshop. , 2011-12-07, http://www-nlpir.nist.gov/projects/tv2011/notebookpapers.html

{AT&T participated in two tasks at TRECVID 2011: content- based copy detection (CCD) and instance-based search (INS). The CCD system developed for TRECVID 2010 was en- hanced for speed and augmented with an additional picture- in-picture detector and alternative audio features. As a pilot task, participation in INS evaluated object-level content- based copy detection and created a basis for integer-score result reranking. This paper reports the enhancements of the CCD system and briefly describe its application to INS for object-level copy detection.}

The definitive version was published in TRECVID 2010 , 2010-11-15

{AT&T participated in two tasks at TRECVID 2010: content- based copy detection (CCD) and instance-based search (INS). The CCD system developed for TRECVID 2009 was en- hanced for efficiency and scale and was augmented by audio features. As a pilot task, participation in INS was meant to evaluate a number of algorithms traditionally used for search in a fully automated setting. In this paper, we report the enhancement of our CCD system and propose a system for INS that attempts to leverage retrieval techniques from different audio, video, and textual cues.}

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