{"id":"W1533986179","doi":"10.1007/978-3-642-14932-0_17","title":"A New Hierarchical Key Frame Tree-Based Video Representation Method Using Independent Component Analysis","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Key frame; Computer science; Artificial intelligence; Key (lock); Computer vision; Frame (networking); Tree structure; Tree (set theory); Video compression picture types; Reference frame; Representation (politics); Video tracking; Video processing; Component (thermodynamics); Pattern recognition (psychology); Feature (linguistics); Cluster analysis; Shot (pellet); Algorithm; Mathematics; Binary tree","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":[0.000592149,0.001446371,0.001694446,0.002875841,0.0006455145,0.001229748,0.001940109,0.0009145514,0.004883512],"category_scores_gemma":[0.001544175,0.0005843677,0.00135296,0.003366427,0.0003512991,0.001891657,0.0009742541,0.001462241,0.003830953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004739017,"about_ca_system_score_gemma":0.001133841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006304146,"about_ca_topic_score_gemma":0.007160449,"domain_scores_codex":[0.9991048,0.0001217557,0.00005249349,0.0002175722,0.0004344789,0.00006878405],"domain_scores_gemma":[0.9991928,0.0001578708,0.00004771582,0.00008023671,0.0004758583,0.00004548544],"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":[0.0001987403,0.00008334377,0.0001752608,0.0001606051,0.00008775405,0.0000482608,0.00004856987,0.00782246,0.04602065,0.002438329,0.00747186,0.9354442],"study_design_scores_gemma":[0.00004942407,0.0001561242,0.001257579,0.00003244597,0.0001875916,0.0002678591,0.00006211014,0.9310422,0.04719267,0.003521868,0.01614567,0.00008441728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009395726,0.0001585566,0.9972522,0.00002311687,0.00005402316,0.00004497106,0.0000890191,0.00110336,0.0003351434],"genre_scores_gemma":[0.01515259,0.0003628577,0.9810787,0.00004607441,0.00006563761,0.0001629313,0.0006609091,0.0002962824,0.002173925],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006304146,"threshold_uncertainty_score":0.01633692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02443334668192619,"score_gpt":0.297231018180797,"score_spread":0.2727976714988708,"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."}}