Introduction to the Eighth International Workshop on Multimedia Data Mining

摘要

Multimedia information is ubiquitous and essential in many applications from homeland security to medicine and bioinformatics. As evidenced by the success of the previous editions of MDM/KDD, there is an increasing need in new techniques and tools that can detect and discover patterns, in multimedia data, that can lead to new knowledge. For example, tools are needed for discovering relationships between objects or segments within images, classifying images based on their content, extracting patterns in sound, categorizing speech and music, and recognizing and tracking objects in video streams. There is also an increasing interest in the real-time analysis of multimedia data generated by distributed sensory applications and ambient intelligence environments.

MDM is a leading venue where researchers, both from the academia and industry, can exchange and compare both relatively mature and green house theories, methodologies, algorithms and frameworks for multimedia data mining. To address this aim, the workshop brings together experts in the analysis of digital media content, multimedia databases, multimedia information retrieval, and domain experts from different applied disciplines with potential in multimedia data mining and knowledge discovery. The previous seven workshops have been held in conjunction with KDD 2000 (Boston, MA), KDD2001 (San Francisco, CA), KDD 2002 (Edmonton, Canada), KDD 2003 (Washington, DC), KDD 2004 (Seattle, WA), KDD 2005 (Chicago, IL), and KDD 2006 (Philadelphia, PA) respectively. Like the previous editions, MDM 2007 aimed at facilitating cross-disciplinary exchange of ideas.

MDM 2007 contains six regular papers focusing on various aspects of multimedia data mining, including content-supported learning of user preferences, integration of multiple visual learners for media mining, salient-point based image object detection, the use of relevance feedback for video event mining, association rule mining for infrequent database items, and a novel algebra for supporting ranked data processing in multimedia mining applications. In addition, we have invited two leading researchers to present their views on major challenges faced by the multimedia data mining community: The invited talk by Dr. Belle Tseng from NEC Labs, America, focuses on the problem of temporal graph mining and its applications to blog analysis. Prof. Keogh from University of California, Riverside, on the other hand, highlights the challenges faced by the data mining community due to frequently non-reproducible results published at the best data mining conferences.

We are thankful to the authors who have submitted papers to the workshop, to our invited speakers, to the PC members and reviewers who all have done a fabulous job, to KDD 2007 organizers (especially to Qiang Yang from University of Science and Technology, Hong Kong for his support to the workshop and to Scott Gaffney for his help with the proceedings).