Robust Action Recognition Using Multi-Scale Spatial-Temporal Concatenations of Local Features as Natural Action Structures
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{"title"=>"Robust Action Recognition Using Multi-Scale Spatial-Temporal Concatenations of Local Features as Natural Action Structures", "type"=>"journal", "authors"=>[{"first_name"=>"Xiaoyuan", "last_name"=>"Zhu", "scopus_author_id"=>"55867823300"}, {"first_name"=>"Meng", "last_name"=>"Li", "scopus_author_id"=>"57189220970"}, {"first_name"=>"Xiaojian", "last_name"=>"Li", "scopus_author_id"=>"55377233700"}, {"first_name"=>"Zhiyong", "last_name"=>"Yang", "scopus_author_id"=>"7405434139"}, {"first_name"=>"Joe Z.", "last_name"=>"Tsien", "scopus_author_id"=>"7003786684"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"23056403", "doi"=>"10.1371/journal.pone.0046686", "issn"=>"19326203", "scopus"=>"2-s2.0-84867136880", "pui"=>"365784436", "sgr"=>"84867136880"}, "id"=>"865f45cf-430d-30a2-90a7-883272fc68d7", "abstract"=>"Human 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.", "link"=>"http://www.mendeley.com/research/robust-action-recognition-using-multiscale-spatialtemporal-concatenations-local-features-natural-act", "reader_count"=>7, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>2}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>2, "Researcher"=>3, "Student > Ph. D. Student"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Neuroscience"=>1, "Psychology"=>2, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Neuroscience"=>{"Neuroscience"=>1}, "Psychology"=>{"Psychology"=>2}, "Computer Science"=>{"Computer Science"=>3}}, "reader_count_by_country"=>{"United Kingdom"=>2}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/564708"], "description"=>"<p>(A), Confusion matrix of the performance of the SVM with NASs on the KTH dataset. The average accuracy is 92.7%. (B), Confusion matrix of the performance of the SVM with NASs on the Weizmann dataset. The average accuracy is 96.7%.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "computer science", "mathematics"], "article_id"=>235198, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g010", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Confusion_matrices_/235198", "title"=>"Confusion matrices.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:26:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/564794"], "description"=>"<p>(A), Locations of the NASs that share the same topic in the LDA model. Two topics are shown for each action in the KTH dataset. (B), Number of NASs share the same topic of the LDA models of the six actions in the KTH dataset. The insets show the numbers of NASs of the selected topics that are shared by the 1–6 actions in the dataset. Note that, by definition, topics are not shared by two or more actions.</p>", "links"=>[], "tags"=>["topics"], "article_id"=>235290, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g011", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Action_topics_in_LDA_/235290", "title"=>"Action topics in LDA.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:28:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/564558"], "description"=>"<p>(A), One-vs.-One discriminative NASs in three pairs of actions in the KTH dataset. The locations and NASs in each individual action are indicated by the same color. (B), Same format as (A) for the Weizmann dataset. Note that each NAS is a set of patch sequences and also that each NAS shown is the average of all patch sequences that share the same structural indices.</p>", "links"=>[], "tags"=>["discriminative"], "article_id"=>235047, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g008", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_One_vs_One_discriminative_natural_action_structures_/235047", "title"=>"One-vs.-One discriminative natural action structures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:24:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/564999"], "description"=>"<p>Performance of the proposed models and the state-of-the-art models.</p>", "links"=>[], "tags"=>["models", "state-of-the-art"], "article_id"=>235496, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_proposed_models_and_the_state_of_the_art_models_/235496", "title"=>"Performance of the proposed models and the state-of-the-art models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-04 01:31:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/563981"], "description"=>"<p>First, we sampled sequences of circular patches at three coupled spatial-temporal scales centered at selected points of interest from videos of human actions. Second, we performed ICA on the patch sequences, fitted Gabor functions to ICs, and collapsed the ICs into a set of clusters. For each IC cluster, we computed a feature, defined as the root mean square amplitudes of the ICs in the cluster, and obtained a feature space. Finally, we digitized the feature space into a set of non-overlap regions using the K-means method, assigned an index to each region. We applied this procedure at the three spatial-temporal scales separately, concatenated the three indices, and designated all patch sequences that shared the same indices as a NAS. Note that each NAS is a set of patch sequences and also that each NAS shown is the average of all patch sequences that share the same structural indices. For illustration purposes, the numbers of video frames are down-sampled to 3, 5, and 7 for the three spatial-temporal scales.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "computer science", "mathematics"], "article_id"=>234467, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Natural_action_structures_/234467", "title"=>"Natural action structures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:14:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/564409"], "description"=>"<p>(A), Correlation coefficients between NASs. (B), The number of NASs that are correlated with (correlation coefficient > = 0.5) a certain number of other NASs. (C), Each curve indicates the One-vs.-Rest classification accuracy for each action. In (A), NASs are indexed sequentially by the occurrences in boxing, handclapping, handwaving, jogging, running, and walking. (A–C) are for the KTH dataset. (D–F), Same format as (A–C) for the Weizmann dataset. In (F), structures are indexed sequentially by the occurrences in bend, jack, pjump, wave1, wave2, jump, run, side, skip, and walk.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "computer science", "mathematics"], "article_id"=>234897, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistics_of_NASs_/234897", "title"=>"Statistics of NASs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:21:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/564167"], "description"=>"<p>(A), 6 frequent NASs compiled from each of the 4 actions in the KTH dataset. The locations of the NASs in the videos and the NASs are indicated by the same color. (B), Same format as (A). 6 frequent NASs compiled from each of the 4 actions in the Weizmann dataset. Note that each NAS is a set of patch sequences and also that each NAS shown is the average of all patch sequences that share the same structural indices.</p>", "links"=>[], "tags"=>["Computational biology", "neuroscience", "computer science", "mathematics"], "article_id"=>234658, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g005", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Examples_of_natural_action_structures_/234658", "title"=>"Examples of natural action structures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:17:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/564658"], "description"=>"<p>The nodes represent random variables. The shaded nodes are observed variables and the unshaded nodes indicate unobserved variables. The plates indicate repetitions. <i>M</i> is the number of video sequences of each action, <i>N<sub>d</sub></i> is the number of code words <i>w</i> in the <i>d</i>-th video sequence, and <i>z</i> is the hidden topic. The parameters of the model include <i>α</i>, the parameter of the Dirichlet distribution, <i>θ</i>, the parameter of the multinomial distribution, and <i>β</i>, which parameterizes the multinomial distribution conditioned on topic <i>z</i>.</p>", "links"=>[], "tags"=>["dirichlet", "allocation"], "article_id"=>235151, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g009", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Latent_Dirichlet_Allocation_model_/235151", "title"=>"Latent Dirichlet Allocation model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:25:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/563661"], "description"=>"<p>(A), Multi-scale neural computation principle. (B), Illustration of sequential activations of different neurons (N1, N2, and N3) during the action of “running”.</p>", "links"=>[], "tags"=>["multi-scale", "neural", "computation"], "article_id"=>234149, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g001", "stats"=>{"downloads"=>3, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_multi_scale_neural_computation_principle_in_the_primary_visual_cortex_/234149", "title"=>"Illustration of the multi-scale neural computation principle in the primary visual cortex.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:09:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/564309"], "description"=>"<p>(A), Examples of NASs shared by 1 to 6 actions in the KTH dataset. The numbers are the percentages of the NASs shared by 1 to 6 actions. (B), Same format as (A) for the Weizmann dataset. Note that each NAS is a set of patch sequences and also that each NAS shown is the average of all patch sequences that share the same structural indices.</p>", "links"=>[], "tags"=>["occurrences"], "article_id"=>234798, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g006", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistics_of_occurrences_of_NASs_/234798", "title"=>"Statistics of occurrences of NASs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:19:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/563814"], "description"=>"<p>(A), Six actions in four conditions in the KTH dataset. The four conditions are, from top to bottom, outdoor (C1), outdoor with variations in image scale (C2), outdoor with different cloths (C3), and indoor (C4). (B), Ten actions in the Weizmann dataset.</p>", "links"=>[], "tags"=>["kth", "weizmann"], "article_id"=>234309, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Actions_in_the_KTH_and_the_Weizmann_datasets_/234309", "title"=>"Actions in the KTH and the Weizmann datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:11:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/299553", "https://ndownloader.figshare.com/files/299637"], "description"=>"<div><p>Human 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.</p> </div>", "links"=>[], "tags"=>["robust", "multi-scale", "spatial-temporal", "concatenations", "features", "structures"], "article_id"=>119063, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0046686.s001", "https://dx.doi.org/10.1371/journal.pone.0046686.s002"], "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Robust_Action_Recognition_Using_Multi_Scale_Spatial_Temporal_Concatenations_of_Local_Features_as_Natural_Action_Structures/119063", "title"=>"Robust Action Recognition Using Multi-Scale Spatial-Temporal Concatenations of Local Features as Natural Action Structures", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-10-04 02:31:03"}
  • {"files"=>["https://ndownloader.figshare.com/files/564069"], "description"=>"<p>(A), ICs of the three patch sequences sampled at three scales (Scale1, Scale2, and Scale3) from the KTH dataset. (B), fitted Gabor functions of the ICs shown in A. (C) and (D) are the same as (A) and (B) respectively for the Weizmann dataset. Note that, in each figure from top to bottom, the actual sizes of patch sequences are increased and the sequences at larger sizes are down-sampled.</p>", "links"=>[], "tags"=>["gabor", "functions"], "article_id"=>234565, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fitting_Gabor_functions_to_ICs_/234565", "title"=>"Fitting Gabor functions to ICs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:16:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/565039"], "description"=>"<p>Performance of the proposed models and the baseline models.</p>", "links"=>[], "tags"=>["models", "baseline"], "article_id"=>235535, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_proposed_models_and_the_baseline_models_/235535", "title"=>"Performance of the proposed models and the baseline models.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-04 01:32:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/564915"], "description"=>"<p>Spatial distributions of NASs in the 6 actions in the KTH dataset. The panels show the normalized frequencies of the NASs shown to the left, in the three areas indicated by the numbers on the video frame. The top-left figure shows the partition grids. Note that each NAS is a set of patch sequences and also that each NAS shown is the average of all patch sequences that share the same structural indices.</p>", "links"=>[], "tags"=>["distributions"], "article_id"=>235410, "categories"=>["Information And Computing Sciences", "Mathematics", "Biological Sciences", "Neuroscience"], "users"=>["Xiaoyuan Zhu", "Meng Li", "Xiaojian Li", "Zhiyong Yang", "Joe Z. Tsien"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0046686.g012", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spatial_distributions_of_NASs_/235410", "title"=>"Spatial distributions of NASs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-04 01:30:10"}

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Relative Metric

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