[A Gaussian mixture-hidden Markov model of human visual behavior]. Article Swipe
Huaqian Liu
,
Xiujuan Zheng
,
Yan Wang
,
Yun Zhang
,
Kai Liu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.7507/1001-5515.202008022
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.7507/1001-5515.202008022
Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://pubmed.ncbi.nlm.nih.gov/34180197
- OA Status
- green
- Cited By
- 1
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3175367190
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3175367190Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.7507/1001-5515.202008022Digital Object Identifier
- Title
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[A Gaussian mixture-hidden Markov model of human visual behavior].Work title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-06-25Full publication date if available
- Authors
-
Huaqian Liu, Xiujuan Zheng, Yan Wang, Yun Zhang, Kai LiuList of authors in order
- Landing page
-
https://pubmed.ncbi.nlm.nih.gov/34180197Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/9927771Direct OA link when available
- Concepts
-
Hidden Markov model, Pattern recognition (psychology), Mixture model, Computer science, Artificial intelligence, Linear discriminant analysis, Segmentation, Pooling, Autoregressive model, Machine learning, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- References (count)
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11Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| best_oa_location.source.host_organization | https://openalex.org/I1299303238 |
| best_oa_location.source.host_organization_name | National Institutes of Health |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
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| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Sheng Wu Yi Xue Gong Cheng Xue Za Zhi |
| best_oa_location.landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/9927771 |
| primary_location.id | pmid:34180197 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S4306525036 |
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| primary_location.source.is_oa | False |
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| primary_location.source.display_name | PubMed |
| primary_location.source.host_organization | https://openalex.org/I1299303238 |
| primary_location.source.host_organization_name | National Institutes of Health |
| primary_location.source.host_organization_lineage | https://openalex.org/I1299303238 |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi |
| primary_location.landing_page_url | https://pubmed.ncbi.nlm.nih.gov/34180197 |
| publication_date | 2021-06-25 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2015831141, https://openalex.org/W2143800813, https://openalex.org/W1929087671, https://openalex.org/W2023155483, https://openalex.org/W2314020718, https://openalex.org/W2497909917, https://openalex.org/W2007266026, https://openalex.org/W3038780934, https://openalex.org/W2607262101, https://openalex.org/W2094539281, https://openalex.org/W2044228361 |
| referenced_works_count | 11 |
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| abstract_inverted_index.(LDA) | 83 |
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