Submission Open - ICME Workshop on Broadcast and User-generated Content Recognition and Analysis (BRUREC) (jinyu han )

Subject: Submission Open - ICME Workshop on Broadcast and User-generated Content Recognition and Analysis (BRUREC)
From:    jinyu han  <jinyuhan2008@xxxxxxxx>
Date:    Wed, 30 Jan 2013 23:28:44 -0800

Dear list subscriber, Please help us circulate this CfPs widely and forgive any cross-postings. The online submission is now open: To make sure your paper is submitted to BRUREC for review, please select the workshop track '1st IEEE International Workshop on Broadcast and User-Generated Content Recognition and Analysis (BRUREC)' when creating a new submission. On behalf of the BRUREC 2013 Committee, we would like to invite you to be part of the 1st IEEE International Workshop on Broadcast and User-generated Content Recognition and Analysis (BRUREC -, held in conjunction with ICME 2013 (, between July 15-19 in San Jose, California USA. A PDF call for papers is accessible at: If you have any enquiries, please get in touch with me or email jhan at ---------------------------------------------------------------------------------------------- *************** SECOND CALL FOR PAPERS *************** ---------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------ *** 1st IEEE International Workshop on Broadcast and User-generated Content Recognition and Analysis (BRUREC) at ICME 2013*** ------------------------------------------------------------------------------------------------------------------------------ ***** July 15-19, 2013 Fairmont Hotel, San Jose, USA | ***** ------------------------------------------------------------------------------------------------------------------------------ In the past decade, we have seen great advancement in the area of visual and acoustic content recognition and analysis. Audio fingerprinting, for example, has led to many successful commercial applications and fundamentally changed the way people listen to, share and store music. In the meanwhile, research and development in visual content identification has reached a watershed, and large-scale commercial applications have started to emerge. Automated Content Recognition (ACR) applications have found their way into consumer applications. In the meanwhile, major Hollywood movie studios and TV networks have adapted ACR to track and manage their content at large scale. While broadcast quality video and audio analysis is already at an advanced stage, consumer-produced content analysis is not. Great potential exists on the media connectivity between broadcast media and user-generated content. Therefore, in the last few years, user-generated content has attracted increasing attention from both academia and industry. This workshop aims to extend the ICME conference by focusing on algorithms, systems, applications, and standards for content recognition and analysis that can be applied across video and audio domains. BRUREC will cover all aspects on multimedia data generated by broadcast media such as TV and Radio as well as user-generated content such as Youtube and Vimeo. The goal of the workshop is to bring together researchers and practitioners in both industry and academia in the scientific community of content recognition and analysis across video, audio and multimedia domains to discuss the latest advance, challenges and unaddressed problems as well as exchange views, ideas in related technologies and applications, in which we attempt to advance the State of the Art of Broadcast and User-generated Content Recognition and Analysis. --------------------------------------------------------------------- ***** Topics include but are not limited to: ***** --------------------------------------------------------------------- Content recognition and analysis algorithms and techniques: - Video and audio fingerprinting for content identification - Segmentation and classification of audio and visual content - Image classification and recognition - Features and descriptors for video and audio content - Audio and visual content clustering - Large database indexing, matching, and search - Machine learning for content classification - Evaluation of content-based identification and classification Content identification systems and applications: - Automated Content Recognition (ACR) - TV-centric content analysis and recognition - Emerging standards related to visual and audio content identification - Automatic content recognition from TV or Radio - Implementation of content recognition systems and services - Content identification in mobile devices - Other content recognition based applications (e.g., recommendation and ad targeting) ---------------------------------------------------------- ********** Important Dates ********** ---------------------------------------------------------- -Paper Submission: March 7, 2013 -Notification of Acceptance: April 15, 2013 -Camera-Ready Paper Due: April 30, 2013 ---------------------------------------------------------- ********** Organizers ********** ---------------------------------------------------------- Jinyu Han Gracenote, Inc. jhan@xxxxxxxx Gerald Friedland ICSI, UC Berkeley fractor@xxxxxxxx Peter Dunker Gracenote, Inc. pdunker@xxxxxxxx ------------------------------------------------------------------------ ********** Technical Program Committee ********** ------------------------------------------------------------------------ Ching-Wei Chen (Gracenote, USA) Jingdong Chen (Northwestern Polytechnical University, China) Ngai-Man Cheung (Singapore University of Technology and Design, Singapore) Oscar Celma (Gracenote, USA) Trista Chen (Cognitive Networks, USA) Roger Dannenberg (Carnegie Mellon University, USA) Lixin Duan (SAP Research, Singapore) Yuan Dong (Orange Labs, France Telecom China) Zhiyao Duan (Northwestern University, USA) Dan Ellis (Columbia University, USA) Matthias Gruhne (Bach Technologies, Germany) Peter Grosche (Huawei European Research Center, Germany) Congcong Li (Google, USA) Lie Lu (Dolby Labs, China) Xiaofan Lin (, USA) Gautham Mysore (Adobe, USA) Bryan Pardo (Northwestern University, USA) Regu Radhakrishnan (Dolby Labs, USA) Paris Smaragdis (University of Illinois at Urbana-Champaign, USA) George Tzanetakis (University of Victoria, Canada) Junsong Yuan (Nanyang Technological University, Singapore) Honggang Zhang (Beijing University of Posts and Telecommunications, China)

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