We are delighted to welcome you to Workshop on Wearable Multimedia (WearMMe 2017) held in conjunction with ACM International Conference on Multimedia Retrieval (ICMR) in Bucharest, Romania.
There has been substantial progress to date in developing computing devices and sensors that can be easily carried on the body. The last few years have also been marked by some notable achievements in learning from sensory data. This unique combination poses research challenges and opportunities for the next future of wearable computing. We believe wearable computing will be a very prominent research field for the multimedia and other communities. As such, there is a compelling need for science and technology that enable devices, algorithms, and humans to interact to achieve humanistic intelligence reciprocally. The range of real-world examples and applications of wearable is large and spans from the web and social applications (e.g. egocentric search engines, recommendation systems, and personalization), to medical robotics (e.g. assistive devices, bionic limbs and exoskeletons).
The aim of this workshop is to bring together experts from various research communities including multimedia, computer vision, human-computer interaction, robotics, and machine learning to share recent advances and explore the future research. Toward this end, we are proud to have organized an exciting program in this half-day event. We are pleased to have Associate Professor Yusuke Sugano of Osaka University in Japan to give a keynote speech on appearance-based gaze estimation from ubiquitous cameras. We are also fortunate to have Dr. Kyriaki Kalimeri of ISI Foundation in Italy to share her recent work on identifying urban mobility challenges for the visually impaired with mobile monitoring of multimodal bio-signals. Last but not least, we are pleased to have three stimulating presentations selected from papers submitted to the workshop.
Finally, we wish all the attendees a highly stimulating, informative, and enjoyable workshop.
Proceeding Downloads
On the Exploitation of Hidden Markov Models to Improve Location-Based Temporal Segmentation of Egocentric Videos
Wearable cameras allow to easily acquire long and unstructured egocentric videos. In this context, temporal video segmentation methods can be useful to improve indexing, retrieval and summarization of such content. While past research investigated ...
Wearable for Wearable: A Social Signal Processing Perspective for Clothing Analysis using Wearable Devices
Clothing conveys a strong communicative message in terms of social signals, influencing the impression and behaviour of others towards a person; unfortunately, the nature of this message is not completely clear, and social signal processing approaches ...
Semi-Automatic Annotation with Predicted Visual Saliency Maps for Object Recognition in Wearable Video
Recognition of objects of a given category in visual content is one of the key problems in computer vision and multimedia. It is strongly needed in wearable video shooting for a wide range of important applications in society. Supervised learning ...