{"id":"W2138476053","doi":"10.1109/icmla.2009.32","title":"Video Copy Detection Using Temporally Informative Representative Images","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Hash function; Artificial intelligence; Computer vision; Robustness (evolution); Dynamic perfect hashing; Video tracking; Block-matching algorithm; Pattern recognition (psychology); Hash table; Video processing; Double hashing","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":[0.0004931208,0.000490947,0.0006263524,0.002138608,0.0002485465,0.0005681401,0.0006822951,0.0005248112,0.0007632028],"category_scores_gemma":[0.002782261,0.0002783458,0.0003867727,0.001041785,0.0003837984,0.001306467,0.0007483092,0.0003498451,0.0005015879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003414187,"about_ca_system_score_gemma":0.0003784374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004875047,"about_ca_topic_score_gemma":0.0005687382,"domain_scores_codex":[0.9994747,0.00007460916,0.00002724189,0.000109105,0.0002701163,0.0000442234],"domain_scores_gemma":[0.9987796,0.000259121,0.0002990097,0.0002700797,0.0003302547,0.00006199398],"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":[0.0008934278,0.0001045473,0.008051333,0.0003754731,0.0001347595,0.0007129416,0.0003073298,0.01315914,0.2635708,0.003798491,0.002366769,0.7065248],"study_design_scores_gemma":[0.00007546318,0.001130957,0.02041127,0.00007336833,0.0001917685,0.006785147,0.0003936682,0.3868883,0.5684246,0.003495142,0.01198184,0.0001484852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2351806,0.001187041,0.7588832,0.0001241546,0.0001071759,0.0001890396,0.0002603199,0.001886853,0.002181643],"genre_scores_gemma":[0.6709054,0.0007205533,0.3256389,0.00006539103,0.00009833767,0.00007338683,0.000439218,0.0000757566,0.001983062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002138608,"threshold_uncertainty_score":0.002607942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605006814229748,"score_gpt":0.2809996741277888,"score_spread":0.2649496059854913,"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."}}