{"id":"W1977383060","doi":"10.1109/ism.2011.58","title":"Shot Boundary Detection Using Genetic Algorithm Optimization","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Computer science; Metric (unit); Genetic algorithm; Shot (pellet); Convergence (economics); Heuristic; Boundary (topology); Enhanced Data Rates for GSM Evolution; Precision and recall; Edge detection; Artificial intelligence; Algorithm; Pattern recognition (psychology); Computer vision; Image (mathematics); Machine learning; Image processing; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001174479,0.001115211,0.00141891,0.002176024,0.0004873268,0.00104433,0.001351466,0.001292618,0.001024264],"category_scores_gemma":[0.003777381,0.0004742919,0.0007236,0.001141429,0.0006717296,0.0008353295,0.0006890229,0.0007990436,0.0003268518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105951,"about_ca_system_score_gemma":0.001188002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00630905,"about_ca_topic_score_gemma":0.004625918,"domain_scores_codex":[0.9991469,0.0002433178,0.00003661982,0.000176543,0.0003350422,0.00006154062],"domain_scores_gemma":[0.998965,0.0005788045,0.0001241665,0.00005141279,0.0002510806,0.00002950145],"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":[0.00008204378,0.0001082082,0.001143649,0.00008101141,0.0001004926,0.00008572396,0.0001085095,0.7159912,0.01121041,0.006476937,0.001189953,0.2634218],"study_design_scores_gemma":[0.000009187655,0.00003093739,0.0001550995,0.0000050843,0.000009677611,0.00001921184,0.000009703043,0.9968985,0.001226091,0.001294468,0.0003356691,0.000006376094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01585712,0.0001550495,0.9820984,0.00006511436,0.00002459105,0.0000606106,0.00001804374,0.0005676362,0.00115342],"genre_scores_gemma":[0.2353319,0.0001650527,0.7619127,0.0001111733,0.00003945323,0.0002284344,0.000160941,0.0001708558,0.001879482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00630905,"threshold_uncertainty_score":0.01254469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0385976578761106,"score_gpt":0.2327165130138793,"score_spread":0.1941188551377687,"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."}}