Scale and translation invariant learning of spatio-temporal patterns using longest common subsequences and spiking neural networks

The ability to detect human actions or gestures is key for a wide range of applications that involve interactions between humans and robots. These actions are patterns that have a particular spatio-temporal structure. This paper presents an approach for encoding such patterns using spike-timing networks with axonal conductance delays. The proposed method brings the following contributions: first, it enables the encoding of patterns in an unsupervised manner. Second, it allows us to create models of specific patterns using a very small set of training samples, in contrast with standard pattern recognition approaches that typically require large amounts of training data. Based on these models, the method further enables classification of new patterns using a longest-common subsequence approach for matching between patterns of activated neurons. Third, the approach is invariant to scale and translation and thus it enables generalization across multiple scales and positions. Fourth, the approach also enables early recognition of patterns from only partial information about the pattern. The proposed method is validated on a set of gestures representing the digits from 0 to 9, extracted from video data of a human drawing the corresponding digits. The results are also compared with other state of the art pattern recognition algorithms.