Similarity Measure for Multi-attribute Data

Efficient recognition of haptic data such as 3D motion capture data and sign language sensory data can have wide applications in the interactive computer animation and sign language automatic translation areas. For this purpose, we propose a similarity measure for multi-attribute haptic data, a new form of multimedia signal. The proposed similarity measure, based on singular value decomposition, captures the most important features of the signal data, allows for different signal generating rates and reasonable variations in similar signals. Experiments with real life and synthetic data demonstrate that the proposed similarity measure can capture the similarities of motions with different speeds and different lengths and can have up to 100% recognition rates.