Lifelog Search Challenge (LSC) 2019

The Lifelog Search Challenge (LSC 2019) has been performed this Monday, June 10th, 2019 in Ottawa, Canada, at the ACM International Conference on Multimedia Retrieval (ICMR 2019). It was a great success – nine teams were competing against each other for 20 queries (10 for experts and another 10 for novice users) that were selected from an entire month of data, collected by a true lifelogger.

The winning team was the vitrivr team from Basel, Switzerland (Luca Rossetto, Ralph Gasser, Silvan Heller, Mahnaz Amiri Parian and Heiko Schuldt). Congratulations!!!

1st International Workshop on Video Retrieval Methods and Their Limits (ViRaL19) at ICCV 2019

We are organizing a workshop at the ICCV 2019 conference (International Conference on Computer Vision) on video retrieval methods and their limits. We invite papers of up to 4 pages length (excluding references, but including figures), formatted according to the ICCV template ( Submissions shall be single blind, i.e. do not need to be anonymized. The workshop proceedings will be archived in the IEEE Xplore Digital Library and the CVF.Open Acess.

Important dates:

  • Workshop paper submission deadline : July 26, 2019
  • Notification to authors : August 22, 2019
  • Workshop camera-ready submission : August 30, 2019

More information here:
1st International Workshop on Video Retrieval Methods and Their Limits (ViRaL 19)

Special Session Medical Image Mining and Health (MIME)

To be held at the International Conference on Content-Based Multimedia Indexing (CBMI 2019)
4-6 September 2019 / Dublin, Ireland

More information here!

Authors are invited to submit full length papers (6 pages in IEEE double-
column format). Each submission will be peer-reviewed by 2-3 PC members (single-blind).

Topics of interest include (but are not limited to):
• Multimedia data mining in medicine or health
• Medical image and video analysis/indexing
• Causal research and complications investigations by image and video analysis
• Image and video retrieval for medicine
• Case retrieval in medicine by multimedia data
• Multimedia analytics for medicine or health
• Surgical quality assessment by multimedia data analysis and mining
• Health sensor data analysis and mining
• Patient monitoring through sensors
• Multimodal interaction in the health domain
• Speech and audio analysis and retrieval for health applications
• Facial analysis and gesture recognition of patients
• Fusion of multimedia information for health and care-giving applications
• Semantic web approaches for multimedia health applications
• Visual analytics for human machine interaction in the health domain

Important dates:
– Paper submission: 20 May, 2019 (extended)
– Notification of acceptance: 18 June, 2019
– Camera-ready papers due: 29 June, 2019

LSC 2019 @ ICMR 2019

The second Lifelog Search Challenge (LSC 2019) will take place at ICMR 2019 in Ottawa, Canada in June 2019. Just like in 2018, LSC 2019 will be a highly interactive and entertaining workshop modelled on the successful Video Browser Showdown annual competition at the MMM conference. LSC is a participation workshop, which means that all participants will write and present a paper, as well as taking part in the live interactive search challenge. Consequently, the workshop will have two parts, (1) oral presentations, and (2) the search challenge.

More information can be found here:

8th VBS with Great Success

The 8th Video Browser Showdown (VBS) took place a week ago (on January 8th and 9th) at MMM2019 in Thessaloniki, and it was a great success. For the first time we used the V3C1 dataset (Part 1 of the Vimeo Creative Commons Collection), which consists of 7475 video files that amount for about 1000 hours of content. The six participating teams could solve all visual and textual Known-Item Search (KIS) tasks, as well as all Ad-Hoc Video Search (AVS) tasks within a short amount of time! The teams have clearly demonstrated that their sophisticated video retrieval systems are very powerful and allow fast and effective content-based search in videos. We look forward to the next VBS in January 2020 in Daejeon, Korea at MMM2020! More information here:

FWF Research Project SQUASH Approved

I am very happy that a new research project on Surgical Quality Assessment in Gynecologic Laparoscopy (SQUASH) – in collaboration with the Medical University of Vienna – has been approved by the FWF Austrian Science Fund. In this research project we will investigate if we can improve the efficiency and feasibility of technical skill assessment by automatic video content analysis, i.e, by content-based video retrieval and current methods of machine learning/deep learning.

We consider this research project as a pioneering work in the interdisciplinary overlap of computer- and medical science, which will investigate fundamental research questions that should provide the basis for future computer-aided surgical quality assessment (SQA). We expect that our research results will help to significantly facilitate the currently cumbersome and error-prone SQA process, and hence enable more surgeons to actually perform error ratings.

More information is available here.

Detecting Semantics in Endoscopic Videos with Deep Neural Networks

Here are the slides of my talk given at the 4th European Congress on Endometriosis (EEC2018) in Vienna, Austria, on November 23, 2018.

Interactive Video Search: Where is the User in the Age of Deep Learning? @ ACMMM18

Here are the slides of our tutorial presented on Monday, October 23, 2018 at ACM Multimedia 2018 in Seoul.

The Importance of Medical Multimedia @ACMMM18

Here are the slides of our tutorial presented on Monday, October 23, 2018 at ACM Multimedia 2018 in Seoul.

Residual Motion Improves Deep Learning Performance for Surgical Actions in Gynecologic Laparoscopy

In a recent work, presented at the 31st IEEE International Symposium on Computer-Based Medical Systems (CBMS2018), we could show that the inclusion of Residual Motion improves classification performance of surgical actions in videos from gynecologic laparoscopy significantly (resulting in a boost of Recall and Precision of 5% and 9% with the GoogLeNet CNN architecture). This performance can be improved even further (to a boost of 13% and 25% in terms of Recall and Precision) by using a late fusion approach for frame classification in the videos. The corresponding paper can be found here.