1. Jana Machajdik, Allan Hanbury, Julian Stöttinger. Understanding Affect in Images.

2. Wei Song, Dian Tjondronegoro, Ivan Himawan. ROI-based Content Adaptation for Mobile Device Usage of Video Conferencing.

3. Julien Law-To, Gregory Grefenstette, Jean-Luc Gauvain, Guillaume Gravier, Lori Lamel, Julien Despres. Introducing topic segmentation and segmented-based browsing tools into a content based video retrieval system.

Congratulations to the winners and hope to see you all again next year!

-Cees & Malcolm

• Significantly address one of the industry grand challenges.
• Depict working, presentable systems or demos.
• Describe why the system presents a novel and interesting solution.

The 2010 Multimedia Grand Challenge will feature the following presentations:

• Gold medal — 1500 USD
• Silver medal — 1000 USD
• Bronze medal — 500 USD

Researchers are encouraged to submit working systems in response to the challenges to win the grand Challenge competition! A number of solutions (perhaps 10-20) will be selected as finalists and invited to describe their work, demonstrate their solution and argue for the paper’s success in the Grand Challenge Session at ACM Multimedia 2010 in Florence.  Each finalist will have several minutes to present their case.  Final winners will be chosen by industry scientists, engineers and business luminaries.

Prepare your submissions according to these guidelines, and submit your grand challenge participation before August 1st.

See you in Florence.

Malcolm & Cees.

Concrete problem

When a user wants to create a CEWE PHOTOBOOK it can be designed with the help of a dedicated software application. This software first assists the user in selecting and laying out the photos from the selected on the pages and add titles and captions. The user may also design the individual page with regard to the number of images, their position, size, rotation and so on. A concept not only present in the CEWE PHOTOBOOK software is the use of different styles for different kinds of photo books. Such a style influences how photos are laid out on the pages, which backgrounds are chosen or which text font is used for headings and captions. In the CEWE PHOTOBOOK software about 100 styles are available to suit different user tastes and different types of photo books. These styles are defined and designed by skilled designers being experts in both general layout principles and understanding photo book user’s needs. However, albeit being a huge enhancement over pure manual layout, the user still has to manually select the style for his photo set out of a large and potentially overwhelming set of styles.

The challenge

In order to further assist the process of photo book design CEWE is seeking for ways simplify this style selection step. The CEWE PHOTOBOOK software today provides different styles for different events (such as party, holiday, chronicle, or family events), for different seasons (such as Christmas, summer, Easter) but also meeting design styles (classical, funky, cute, …). These styles are organized in different categories. The challenge CEWE wants to address is to support users selecting and assigning the right style to an individual photo set. The goal is, however, not necessarily to automatically determine the one and only perfect style, but rather to provide the user with a reasonable selection of styles he or she can choose from. This selection should fit the user’s preferences, the images in the set and the current structure of the photo book, i.e., the distribution of the photos over the single pages.

Data Set and Evaluation

For researchers interested in the described challenge CEWE COLOR will provide selected photo sets together with a list of suitable styles for each set. These list of styles covers the styles currently offered in the CEWE PHOTOBOOK application. This list of styles is organized in a taxonomy, which will also be provided upon request. The developed methods for matching photo sets to photo book styles should be evaluated on the provided photo sets. Please contact Sabine Thieme (Sabine.Thieme@cewecolor.de) for details.

About CeWe

CeWe Color is the Number One services partner for first-class trade brands on the European photographic market. CeWe supplies both stores and Internet retailers (e-commerce) with photographic products.

This challenge focuses on exploring the limits of what is possible in terms of 2D and 3D data extraction from a low-cost camera network for sports. It hopefully provides opportunities for research in areas such as:

• Content & context fusion for improved multimedia access;
• 3D content generation leveraging emerging acquisition channels;
• Immersive multimedia experiences;
• Multimedia, multimodal & deformable objects search

More generally, the data-set and challenge will hopefully facilitate researchers wishing to address the broader issues posed by the increasing availability of such capture technologies, that brings many new exciting challenges (see for example the recent white paper by the Future Media Internet task force that outlines these challenges.

Tennis is chosen as a case study as it is a sporting environment that is relatively easy to instrument with cheap cameras and features a small number of actors (players) who exhibit explosive and rapid sophisticated motion. Video data from an AV network, corresponding to 9 cameras with built in mics, installed around an indoor court capturing real athletes is provided for experimentation purposes. The capture infrastructure is deliberately set-up to model what is feasible for a local tennis club using commercial off-the shelf components i.e. 720 x 680, MPEG-4 25Hz cameras that are not calibrated or synchronized and that share only limited overlapping fields of view. We are interested in submissions that explore the limits of what is possible from such a real-world capture scenario in terms of:

• Player localization on court and tracking through multiple camera views;
• Event-based analysis and human behaviour modeling using multiple views of the same event / activity: one example is robustly classifying every stroke as a serve, backhand, forehand, etc considering fusion across multiple camera views; another example is detecting the game structure automatically (point, game, match).
• 3D reconstruction of the playing arena and/or the players or their actions; an example is using player location and stroke classification to animate an avatar of the player, even coarsely;
• Longitudinal analysis of player activity and motion over an entire training session;
• Novel visualization and feedback mechanisms of any analysis results.

Dataset

Data features audio and video from up to 9 CCTV-like cameras placed at different points around a tennis court. Camera calibration data is provided. The dataset features 2 players involved in competitive training matches. Court measurements and relative camera placement details are also provided. In addition to video information, accelerometer data from inertial measurement units were also captured with each sequence. Two accelerometers units were placed on each player; one on the player’s dominant forearm, and one on their torso (chest). Each provides time-stamped accelerometer, gyroscope and magnetometer data at their location for the duration of the session.

The data set is now available at the following link:

http://www.cdvp.dcu.ie/tennisireland/TennisVideos/acm_mm_3dlife_grand_challenge/

In the meantime, we can already announce one innovation for 2010. We open the challenge for all papers (long, short, and demo) accepted for the ACM Multimedia conference. So if your (accepted) paper is related to one of the challenges, you are elegible for the prize money.  More details on this new procedure later.

Stay tuned!

Malcolm & Cees.

Evaluations can be based on purely video content features as well as a combination of content and metadata features. Features that bring in information from other public data sources can also be used (eg. Object detectors trained on a separate public dataset). Thinking of new (and surprising) features is recommended!

Any dataset that reflects a breath of content is acceptable, and of course, YouTube and Google Video are a recommended source. Particularly, the data should cover most of the common video genres. If the dataset consists of web videos, sharing a list of links and corresponding labels would be ideal for researchers to compare notes. You may want to consult the The YouTube Data API for retrieving video data.

Evaluation

We propose two types of evaluations for this challenge:

• Offline (direct evaluation): Use a labeled test set to measure precision/recall for the ODP categories.
• Online (indirect): Allow users a browse interface for your classifiers and measure how easily they can find some target concepts (e.g., find a basketball scoring scene). Note that the errors of the classifier can be compensated here since a video can appear in multiple categories, so one could conceive of training for different loss functions here.

The ideal target in this case would match the optimal score for human agreement on the dataset.  If 5 raters categorize each video and we have agreement in 92% of the cases, we expect the automatic classifier to hit the same agreement rate.

The challenge is to develop good schema, algorithms, UI, etc., that will be useful for diaries from audio-only through full-featured multimedia. Specializations to certain contexts, as well as generic systems, are all of interest.

The great challenge for Video conferencing vendors is to supply users with a meeting experience that equals or surpasses “in-person” meetings. It is assumed that when meeting experience will be good enough, or even better, the technology could potentially minimize the need for “physical” meetings (at least for business purposes). Such reduction would mean less travel, less cost (to people, organizations, and the planet), better efficiency and better communication.

This challenge focuses on developing new technologies and ideas to surpass the “in-person” meeting experience. In the process a set of subjective and objective measures to evaluate “meeting” experience will be developed. With these measures, alternative solutions could be compared to each other and to in-person meetings, and optimized accordingly.

Dataset

Not required.

Metrics/Evaluation

As noted above, we are hoping for new metrics, objective and subjective, to be developed that capture the meeting experience. It is desired to have a high correlation between the objective and subjective metrics, and that metrics are robust and reliable. Those metrics could be used to compare existing video conferencing solutions, in-person meetings and new technologies suggested.

About Radvision

Radvision (Nasdaq: RVSN) is the industry’s leading provider of products and technologies for unified visual communications over IP, 3G and emerging IMS/Next Generation networks - enabling high definition video-conferencing, converged video telephony services, and scalable desktop–based visual communications.