{"id":"W2100983561","doi":"10.1109/icassp.2011.5946679","title":"Video thumbnail extraction using video time density function and independent component analysis mixture model","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Thumbnail; Codebook; Artificial intelligence; Vector quantization; Quantization (signal processing); Pattern recognition (psychology); Computer vision; Independent component analysis; Video tracking; Feature extraction; Block-matching algorithm; Feature vector; Video processing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005536685,0.0009134231,0.001020046,0.002082812,0.0003107817,0.0007931303,0.000814537,0.0006251173,0.00164149],"category_scores_gemma":[0.002116346,0.0003645617,0.001001104,0.001711123,0.0003627199,0.001623421,0.0005546372,0.000806771,0.000968278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004602019,"about_ca_system_score_gemma":0.0004148206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002230368,"about_ca_topic_score_gemma":0.00194266,"domain_scores_codex":[0.9993302,0.00009325214,0.00005188486,0.0001551635,0.0003262754,0.00004321558],"domain_scores_gemma":[0.9994273,0.0001720067,0.00006289239,0.00008178414,0.0002273509,0.00002863827],"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":[0.0001752713,0.00005007352,0.0005180064,0.0002380257,0.00006867528,0.0001168083,0.00008713734,0.0298122,0.0684512,0.004009357,0.00251427,0.8939589],"study_design_scores_gemma":[0.00002625198,0.0001665367,0.002365914,0.00003767143,0.00008614576,0.000426674,0.00007838538,0.9170617,0.06526622,0.005616333,0.008789796,0.00007839765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003172482,0.0003079706,0.9953779,0.00004028172,0.00003774434,0.00003575839,0.00006793119,0.0006846205,0.0002753681],"genre_scores_gemma":[0.07345591,0.0007238023,0.9231799,0.000063596,0.00007659182,0.00009563384,0.0005743946,0.0001479774,0.001682229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002230368,"threshold_uncertainty_score":0.005491316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02750332641413922,"score_gpt":0.2290621121176861,"score_spread":0.2015587857035469,"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."}}