3D motion retrieval with motion index tree
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Indexing of variable length multi-attribute motion data
Proceedings of the 2nd ACM international workshop on Multimedia databases
A system for analyzing and indexing human-motion databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Hierarchical indexing structure for 3d human motions
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
On spatial quantization of color images
IEEE Transactions on Image Processing
Knowledge discovery from 3D human motion streams through semantic dimensional reduction
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Video Human Motion Recognition Using a Knowledge-Based Hybrid Method Based on a Hidden Markov Model
ACM Transactions on Intelligent Systems and Technology (TIST)
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3D motion capture is a form of multimedia data that is widely used in animation and medical fields (such as physical medicine and rehabilitation where body joint analysis is needed). These applications typically create large repositories of motion capture data and need efficient and accurate content-based retrieval techniques. 3D motion capture data is in the form of multi-dimensional time series data. To reduce the dimensions of human motion data while maintaining semantically important features, we quantize human motion data by extracting Spatial-Temporal Features through SVD and translate them onto a 1- dimensional sequential representation through our proposed sGMMEM (semantic Gaussian Mixture Modeling with EM). Thus, we achieve good classification accuracies for primitive human motion categories (walking 92.85%,run 91.42%,jump 94.11%) and even for subtle categories (dance 89.47%,laugh 83.33%,basketball signal 85.71%,golf putting 80.00%).