Ensemble of Attractor Networks for 2D Gesture Retrieval

This work presents an Ensemble of Attractor Neural Networks (EANN) model for gesture retrieval. 2D single-stroke gestures were captured and tested offline by the ensemble. The ensemble was compared to a single attractor with the same complexity, i.e. with equal connectivity. We show that the ensemble of neural networks improves the gesture retrieval in terms of capacity and quality of the gestures retrieval, regarding the single network. The ensemble was able to improve the retrieval of correlated patterns with a random assignment of pattern subsets to the ensemble modules. Thus, optimizing the ensemble input is a possibility for maximizing the patterns retrieval. The proposed EANN proved to be robust for gesture recognition with large initial noise promising to be robust for gesture invariants.