Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties
Samo za registrovane korisnike
2023
Autori
Gnjatović, MilanMaček, Nemanja
Saračević, Muzafer
Adamović, Saša
Joksimović, Dušan
Karabašević, Darjan
Članak u časopisu (Objavljena verzija)
Metapodaci
Prikaz svih podataka o dokumentuApstrakt
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, whic...h 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
Ključne reči:
multiple sequence alignment / object recognition / uncertainty in vision task / cognitive economyIzvor:
Axioms, 2023, 12, 1, 3-Izdavač:
- Basel, Switzerland : MDPI
Institucija/grupa
JakovTY - JOUR AU - Gnjatović, Milan AU - Maček, Nemanja AU - Saračević, Muzafer AU - Adamović, Saša AU - Joksimović, Dušan AU - Karabašević, Darjan PY - 2023 UR - http://jakov.kpu.edu.rs/handle/123456789/1514 AB - 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 PB - Basel, Switzerland : MDPI T2 - Axioms T1 - Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties VL - 12 IS - 1 SP - 3 DO - 10.3390/axioms12010003 ER -
@article{ author = "Gnjatović, Milan and Maček, Nemanja and Saračević, Muzafer and Adamović, Saša and Joksimović, Dušan and Karabašević, Darjan", year = "2023", 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", publisher = "Basel, Switzerland : MDPI", journal = "Axioms", title = "Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties", volume = "12", number = "1", pages = "3", doi = "10.3390/axioms12010003" }
Gnjatović, M., Maček, N., Saračević, M., Adamović, S., Joksimović, D.,& Karabašević, D.. (2023). Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties. in Axioms Basel, Switzerland : MDPI., 12(1), 3. https://doi.org/10.3390/axioms12010003
Gnjatović M, Maček N, Saračević M, Adamović S, Joksimović D, Karabašević D. Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties. in Axioms. 2023;12(1):3. doi:10.3390/axioms12010003 .
Gnjatović, Milan, Maček, Nemanja, Saračević, Muzafer, Adamović, Saša, Joksimović, Dušan, Karabašević, Darjan, "Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties" in Axioms, 12, no. 1 (2023):3, https://doi.org/10.3390/axioms12010003 . .