{"id":"W2102401677","doi":"10.1109/icassp.2005.1415461","title":"Video Shot Boundary Detection Using Independent Component Analysis","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Independent component analysis; Thresholding; Subspace topology; Computer science; Computer vision; Pattern recognition (psychology); Feature vector; Cluster analysis; Shot (pellet); Object detection; Feature (linguistics); Frame (networking); Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0003342767,0.0001102669,0.0001983203,0.0004815455,0.0002532922,0.0004134249,0.0002989988,0.00005540288,0.00007117934],"category_scores_gemma":[0.000005856194,0.00009823854,0.0002098719,0.001772074,0.00001867088,0.0004844881,0.0001200953,0.00007057854,0.00001922152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001134522,"about_ca_system_score_gemma":0.00003117886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002717024,"about_ca_topic_score_gemma":0.002788572,"domain_scores_codex":[0.9986502,0.00008510469,0.0003181607,0.0003605203,0.0003996314,0.0001864179],"domain_scores_gemma":[0.9993256,0.00002255445,0.0001000031,0.0004060779,0.00009651724,0.0000492687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001930372,0.0005837525,0.2664766,0.00001909416,0.001528999,0.00004850578,0.0003412063,0.5218344,0.1335668,0.03934195,0.0003518859,0.03588754],"study_design_scores_gemma":[0.0001046309,0.00001171558,0.0763201,0.000001455326,0.0001768956,0.000003382104,0.00001028484,0.9164947,0.005052348,0.001097888,0.0005898041,0.0001367741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2367032,0.00003964435,0.7613339,0.00008335747,0.00009501914,0.00004283441,4.535261e-7,0.0000899135,0.001611725],"genre_scores_gemma":[0.9892987,0.000002518515,0.01023536,0.0001203964,0.00005976107,0.000002303466,0.00001405525,0.000004381641,0.0002625482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7525955,"threshold_uncertainty_score":0.4107345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01688726975257301,"score_gpt":0.2398740253127361,"score_spread":0.2229867555601631,"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."}}