Multimedia Computing Group
The Multimedia Computing (MMC) Group develops algorithms for enriching, accessing, and searching large quantities of data. Such algorithms lie at the core of tomorrows’ search engines and large-scale recommender systems. The group sets its focus on developing systems that are oriented to the needs of users, and that solve the challenges faced by large-scale online content and service providers. Multimedia data analytics also has applications in the full range of fields that benefit from data science, including health, telecom, and geosciences.
The group has a track record of developing technologies that make possible optimized interaction with large collections of multimedia data (e.g., images, video, and music) in real-world contexts (e.g., within social networks). Our work requires a combination of mathematical models, machine learning techniques, and practical skills in algorithm development and evaluation. The members of the MMC group share expertise in multimedia information retrieval, recommender systems, multimedia signal processing, social network analysis, human computation (crowdsourcing) and quality of experience. Collaborations include joint work with researchers from Yahoo Labs, Telefónica Research, Microsoft Research and Google.
The work being carried out in the MMC Group encompasses a broad palette of research directions including:
- Multimedia content analysis and search
- Semantics extraction from multimedia data
- Multi-modal query expansion
- Multi-source search result reranking
- Multimedia information retrieval in a social network context
- Modeling information propagation and relationships in social networks
- Collaborative recommender systems
- Social recommendation
- Interaction with multimedia content
- (Affective) User profiling
- User (search/uploader) intent
- Query failure prediction
- Quality of multimedia experience
- Multimedia content management
- Multimedia databases and dataspaces
- Entity retrieval
14 april 2019
Odette Scharenborg published in Speech Communication
The paper provides a systematic review of the literature on non-native spoken-word recognition in the presence of background noise, and posits an updated theory on the effect of background noise on native and non-native spoken-word recognition.
24 maart 2019
Cynthia Liem Tweeting for @NL_Wetenschap
In the week of March 25, Cynthia Liem will be Tweeting through this account, that by now has almost 7500 followers. Follow @NL_Wetenschap to learn more about Cynthia’s activities throughout the week!
17 maart 2019
Information Diffusion Backbones in Temporal Networks
Xiu-Xiu Zhan is giving a contributed talk “Information Diffusion Backbones in Temporal Networks” at Young European Probabilists (YEP 2019) workshop "Information diffusion on random graphs".
27 januari 2019
Odette Scharenborg Elected
Odette Scharenborg was elected onto the IEEE Speech and Language Technology Technical Committee* (IEEE SLTC; period 2019-2021).