Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties

Abstract

This paper introduces a heuristic for multiple sequence alignment aimed at improving real-time object recognition in short video streams with uncertainties. It builds upon the idea of the progressive alignment but is cognitively economical to the extent that the underlying edit dis- tance approach is adapted to account for human working memory limitations. Thus, the proposed heuristic procedure has a reduced computational complexity compared to optimal multiple sequence alignment. On the other hand, its relevance was experimentally confirmed. An extrinsic evaluation conducted in real-life settings demonstrated a significant improvement in number recognition accu- racy in short video streams under uncertainties caused by noise and incompleteness. The second line of evaluation demonstrated that the proposed heuristic outperforms humans in the post-processing of recognition hypotheses. This indicates that it may be combined with state-of-the-art machine learning approaches, which are typically not tailored to the task of object sequence recognition from a limited number of frames of incomplete data recorded in a dynamic scene situation