Fundamentals of speech recognition
Fundamentals of speech recognition
An active service framework and its application to real-time multimedia transcoding
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A Content Model for the Mobile Adaptation of Multimedia Information
Journal of VLSI Signal Processing Systems
Architectures for Personalized Multimedia
IEEE MultiMedia
Applications of Video-Content Analysis and Retrieval
IEEE MultiMedia
Policy-Driven Personalized Multimedia Services for Mobile Users
IEEE Transactions on Mobile Computing
Real-Time Content-Based Adaptive Streaming of Sports Videos
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Affective content detection using HMMs
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A User-Driven Adaptation Strategy for Mobile Video Streaming Applications
ICDCSW '05 Proceedings of the First International Workshop on Services and Infrastructure for the Ubiquitous and Mobile Internet (SIUMI) (ICDCSW'05) - Volume 03
Creating audio keywords for event detection in soccer video
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Large-scale multimodal semantic concept detection for consumer video
Proceedings of the international workshop on Workshop on multimedia information retrieval
Event based indexing of broadcasted sports video by intermodalcollaboration
IEEE Transactions on Multimedia
Affective video content representation and modeling
IEEE Transactions on Multimedia
A unified framework for semantic shot classification in sports video
IEEE Transactions on Multimedia
Audio-Visual Affect Recognition
IEEE Transactions on Multimedia
A user-centered remote control system for personalized multimedia channel selection
IEEE Transactions on Consumer Electronics
End to end QoS provisioning multimedia wireless/mobile networks using an adaptive framework
IEEE Communications Magazine
Quality-of-service routing for supporting multimedia applications
IEEE Journal on Selected Areas in Communications
New architecture for dynamic frame-skipping transcoder
IEEE Transactions on Image Processing
Personalized content adaptation using multimodal highlights of soccer video
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Ifelt: accessing movies through our emotions
Proceddings of the 9th international interactive conference on Interactive television
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Personalized video adaptation is expected to satisfy individual users' needs on video content. Multimedia data mining plays a significant role of video annotation to meet users' preference on video content. In this paper, a comprehensive solution for personalized video adaptation is proposed based on video content mining. Video content mining targets both cognitive content and affective content. Cognitive content refers to those semantic events, which are very specific for the video domains. Sometimes, users might prefer "emotional decision" to select their interested video content. Therefore, we introduce affective content which causes audiences' strong reactions. For cognitive content mining, features are extracted from multiple modalities. Machine learning module is further performed to get some middle-level features, such as specific audio sounds, semantic video shots and so on. Those middle-level features are used to detect cognitive content by using Hidden Markov Models. For affective content mining, affective content is detected with three affective levels: "low", "medium" and "high". Considering affective levels might have no sharp boundaries, fuzzy c mean clustering is used on low-level features to simulate user's perceptions. The adaptation is later implemented based on MPEG-21 Digital Item Adaptation framework. One of the challenges is how to quantify users' preference on video content. Information Entropy (IE) and Membership Functions are calculated to decide priorities for resource allocation for cognitive content and affective content respectively.