{"id":"W1582266017","doi":"10.1007/978-3-540-89796-5_112","title":"Recognizing Human Action from Videos Using Histograms of Visual Words","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Histogram; Representation (politics); Bag-of-words model; Frame (networking); Artificial intelligence; Word (group theory); Feature (linguistics); Action (physics); Bag-of-words model in computer vision; Pattern recognition (psychology); Visual Word; Natural language processing; Action recognition; Computer vision; Image (mathematics); Mathematics; Image retrieval; Linguistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003519384,0.0004196494,0.0005232644,0.0009856448,0.0004199317,0.0002383432,0.001295584,0.0003367824,0.00006541525],"category_scores_gemma":[0.00003480123,0.0004351,0.0001826573,0.0005199042,0.0005017772,0.0009142472,0.0005092463,0.0006938287,0.00002712767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004007998,"about_ca_system_score_gemma":0.0003108196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002582172,"about_ca_topic_score_gemma":0.0001590502,"domain_scores_codex":[0.9968222,0.00004606998,0.0006565957,0.001186429,0.0008590161,0.0004296634],"domain_scores_gemma":[0.9980986,0.000226896,0.0005925808,0.0006915902,0.0002640691,0.0001262233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006012514,0.00006366087,0.00006005446,0.00002728178,0.0000200134,0.00004768362,0.001054904,0.002564918,0.008367505,0.0002447512,0.00001978463,0.9875234],"study_design_scores_gemma":[0.001169127,0.0007559528,0.0005776571,0.002678879,0.00007340757,0.0002571102,0.000002625819,0.7940485,0.07398121,0.1201947,0.003797365,0.002463382],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0155533,0.0002112089,0.9809109,0.00004974572,0.001792975,0.0002375842,0.000005505433,0.0001448429,0.00109392],"genre_scores_gemma":[0.7273909,0.0001004289,0.270658,0.0005210127,0.001083153,0.00000528847,0.00002982425,0.00004966165,0.0001617254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.98506,"threshold_uncertainty_score":0.9998101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06096985200373099,"score_gpt":0.3019910410025445,"score_spread":0.2410211889988135,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}