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In this paper, we propose a systematic approach to building an entirely hierarchical hand posture database. The hierarchy provides the possibility of considering a large number of hand poses while requires a low time-space complexity for construction. Furthermore, two algorithms (random decision forest and exhaustive search) are chosen and tested on this database. We show that by utilizing such a database one will achieve better performances on classifiers’ training and search strategies (two main categories of the algorithms in the field of machine learning) compared with conventional (all-in-one-layer) databases.

Multi-Forest Classification and Layered Exhaustive Search using a Fully Hierarchical Hand Posture/Gesture Database