Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties

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 progressive but is cognitively economical to extent that underlying edit distance approach adapted account human working memory limitations. Thus, proposed procedure has reduced computational complexity compared optimal alignment. On other hand, its relevance was experimentally confirmed. An extrinsic evaluation conducted real-life settings demonstrated significant improvement number accuracy under uncertainties caused by noise and incompleteness. The second line outperforms humans post-processing hypotheses. indicates it may be combined state-of-the-art machine learning approaches, which are typically not tailored task from limited frames incomplete data recorded dynamic scene situation.