{"id":"W2066272345","doi":"10.1109/mmsp.2010.5662069","title":"An efficient framework on large-scale video genre classification","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Codebook; Artificial intelligence; Feature extraction; Histogram; Pattern recognition (psychology); Search engine indexing; Scale-invariant feature transform; Bag-of-words model; Latent Dirichlet allocation; Classifier (UML); Scalability; Categorization; Cluster analysis; Data mining; Topic model; Image (mathematics); Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001315351,0.001194764,0.001913815,0.004625567,0.0009594955,0.001690431,0.002462024,0.001144582,0.002646955],"category_scores_gemma":[0.00364911,0.0004519407,0.00156223,0.004472134,0.0005342708,0.002571075,0.001755263,0.001473051,0.002822651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001145015,"about_ca_system_score_gemma":0.001786417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008855655,"about_ca_topic_score_gemma":0.01005182,"domain_scores_codex":[0.9987822,0.0001683023,0.0001102908,0.0002986582,0.000501945,0.000138591],"domain_scores_gemma":[0.9988839,0.0001906491,0.0001108907,0.0002558713,0.0004560399,0.0001026114],"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":[0.0001670596,0.0002748768,0.001706113,0.0001957854,0.00007893609,0.0002001288,0.0001245471,0.03115129,0.02963323,0.0179132,0.01855706,0.8999977],"study_design_scores_gemma":[0.00003663038,0.0001090868,0.00130496,0.00002098843,0.00004056509,0.0002775488,0.0001081777,0.9602019,0.007881674,0.01948996,0.01048956,0.00003888352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002797915,0.0002644664,0.9934296,0.00008215485,0.00004393287,0.0001338109,0.0002906843,0.002391935,0.0005653952],"genre_scores_gemma":[0.06659712,0.0003986534,0.927107,0.00007879307,0.0001670316,0.0004152271,0.002787532,0.0001630624,0.002285618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008855655,"threshold_uncertainty_score":0.01760817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063174436750559,"score_gpt":0.2651127059455956,"score_spread":0.25448096157809,"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."}}