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dc.contributor.authorZhu, Xiaoyuan
dc.contributor.authorLi, Meng
dc.contributor.authorLi, Xiaojian
dc.contributor.authorYang, Zhiyong
dc.contributor.authorTsien, Joe Z.
dc.date.accessioned2012-10-26T20:35:12Z
dc.date.available2012-10-26T20:35:12Z
dc.date.issued2012-10-4en_US
dc.identifier.citationPLoS One. 2012 Oct 4; 7(10):e46686en_US
dc.identifier.issn1932-6203en_US
dc.identifier.pmid23056403en_US
dc.identifier.doi10.1371/journal.pone.0046686en_US
dc.identifier.urihttp://hdl.handle.net/10675.2/829
dc.description.abstractHuman and many other animals can detect, recognize, and classify natural actions in a very short time. How this is achieved by the visual system and how to make machines understand natural actions have been the focus of neurobiological studies and computational modeling in the last several decades. A key issue is what spatial-temporal features should be encoded and what the characteristics of their occurrences are in natural actions. Current global encoding schemes depend heavily on segmenting while local encoding schemes lack descriptive power. Here, we propose natural action structures, i.e., multi-size, multi-scale, spatial-temporal concatenations of local features, as the basic features for representing natural actions. In this concept, any action is a spatial-temporal concatenation of a set of natural action structures, which convey a full range of information about natural actions. We took several steps to extract these structures. First, we sampled a large number of sequences of patches at multiple spatial-temporal scales. Second, we performed independent component analysis on the patch sequences and classified the independent components into clusters. Finally, we compiled a large set of natural action structures, with each corresponding to a unique combination of the clusters at the selected spatial-temporal scales. To classify human actions, we used a set of informative natural action structures as inputs to two widely used models. We found that the natural action structures obtained here achieved a significantly better recognition performance than low-level features and that the performance was better than or comparable to the best current models. We also found that the classification performance with natural action structures as features was slightly affected by changes of scale and artificially added noise. We concluded that the natural action structures proposed here can be used as the basic encoding units of actions and may hold the key to natural action understanding.
dc.subjectResearch Articleen_US
dc.subjectBiologyen_US
dc.subjectComputational Biologyen_US
dc.subjectNeuroscienceen_US
dc.subjectComputational Neuroscienceen_US
dc.subjectCircuit Modelsen_US
dc.subjectCoding Mechanismsen_US
dc.subjectSensory Systemsen_US
dc.subjectVisual Systemen_US
dc.subjectNeural Networksen_US
dc.subjectComputer Scienceen_US
dc.subjectComputer Modelingen_US
dc.subjectComputing Methodsen_US
dc.subjectComputer Inferencingen_US
dc.subjectEngineeringen_US
dc.subjectHuman Factors Engineeringen_US
dc.subjectMan Computer Interfaceen_US
dc.subjectSignal Processingen_US
dc.subjectVideo Processingen_US
dc.subjectMathematicsen_US
dc.subjectProbability Theoryen_US
dc.subjectBayes Theoremen_US
dc.subjectProbability Distributionen_US
dc.subjectStatisticsen_US
dc.subjectStatistical Methodsen_US
dc.titleRobust Action Recognition Using Multi-Scale Spatial-Temporal Concatenations of Local Features as Natural Action Structuresen_US
dc.typeArticleen_US
dc.identifier.pmcidPMC3464264en_US
dc.contributor.corporatenameBrain & Behavior Discovery Institute
dc.contributor.corporatenameDepartment of Neurology
dc.contributor.corporatenameDepartment of Ophthalmology
refterms.dateFOA2019-04-10T00:58:28Z
html.description.abstractHuman and many other animals can detect, recognize, and classify natural actions in a very short time. How this is achieved by the visual system and how to make machines understand natural actions have been the focus of neurobiological studies and computational modeling in the last several decades. A key issue is what spatial-temporal features should be encoded and what the characteristics of their occurrences are in natural actions. Current global encoding schemes depend heavily on segmenting while local encoding schemes lack descriptive power. Here, we propose natural action structures, i.e., multi-size, multi-scale, spatial-temporal concatenations of local features, as the basic features for representing natural actions. In this concept, any action is a spatial-temporal concatenation of a set of natural action structures, which convey a full range of information about natural actions. We took several steps to extract these structures. First, we sampled a large number of sequences of patches at multiple spatial-temporal scales. Second, we performed independent component analysis on the patch sequences and classified the independent components into clusters. Finally, we compiled a large set of natural action structures, with each corresponding to a unique combination of the clusters at the selected spatial-temporal scales. To classify human actions, we used a set of informative natural action structures as inputs to two widely used models. We found that the natural action structures obtained here achieved a significantly better recognition performance than low-level features and that the performance was better than or comparable to the best current models. We also found that the classification performance with natural action structures as features was slightly affected by changes of scale and artificially added noise. We concluded that the natural action structures proposed here can be used as the basic encoding units of actions and may hold the key to natural action understanding.


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