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Using Spatial Pooler of Hierarchical Temporal Memory for object classification in noisy video streams

This paper focuses on analyzing a Spatial Pooler (SP) of Hierarchical Temporal Memory (HTM) ability for facilitating object classification in noisy video streams. In particular, we seek to determine whether employing SP as a component of the video system increases overall robustness to noise. We have implemented our own version of HTM and applied it to object recognition tasks under various testing conditions. The system is composed of a video preprocessing block, a dimensionality reduction section which contains SP, a histograms collecting module and SVM classifier. Our experiments involve assessing performance of two different system setups (i.e. a version featuring SP and one without it) under various noise conditions with 32-frame video files. In order to make tests fair and repeatable the videos of several 3-D geometric shapes were artificially generated. Subsequently, Gaussian noise of a different intensity was introduced to the videos making them more indistinct. Such an approach mimics real-life scenarios where the system is taught ideal objects and then faces in its normal working conditions the challenge of detecting noisy ones. The results of the experiments reveal the superiority of the solution featuring Spatial Pooler over the one without it. Furthermore, the system with SP performed better also in the experiment without a noise component introduced and achieved a mean F1-score of 0.91 in ten trials.