{"id":"W1719049820","doi":"10.1109/dcc.2006.48","title":"MST for Lossy Compression of Image Sets","year":2006,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Lossy compression; Computer science; Image compression; Data compression; Compression (physics); JPEG 2000; Image (mathematics); Artificial intelligence; Image retrieval; Texture compression; Minimum spanning tree; Computer vision; Image processing; Algorithm","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":[0.00002833404,0.0000324994,0.00004801753,0.00001128663,0.00004146717,0.00002221794,0.0002250641,0.00001226321,0.000007912847],"category_scores_gemma":[8.411142e-7,0.00002427696,0.00002718502,0.00008194341,0.00001501815,0.00009144229,0.00005504896,0.00001571962,0.000006448771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002138456,"about_ca_system_score_gemma":0.000004421786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002838377,"about_ca_topic_score_gemma":0.000005720537,"domain_scores_codex":[0.9996827,0.000003803327,0.00008738324,0.0001019437,0.00004953826,0.0000746191],"domain_scores_gemma":[0.9996946,0.0000411912,0.00003183409,0.0001837573,0.00003374157,0.00001486777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002827919,0.0001316318,0.0002392815,0.00001756155,0.000002344301,5.866661e-7,0.00001353379,0.0005260552,0.08649138,0.699321,0.1849114,0.02834242],"study_design_scores_gemma":[0.0005769933,0.00007131392,0.008350517,0.00002356394,0.000004484416,0.000004813503,0.000004266253,0.6369591,0.1836057,0.1003868,0.0698041,0.0002083429],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0132377,0.00002087752,0.9776487,0.001055452,0.00003945896,0.0001341329,0.000003289485,0.00005094493,0.007809481],"genre_scores_gemma":[0.8431524,0.00000132153,0.1559315,0.00009603762,0.00003606361,0.00001492033,0.000003780711,0.00000213184,0.0007618371],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8299147,"threshold_uncertainty_score":0.09899855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01064980978498881,"score_gpt":0.2633224547261871,"score_spread":0.2526726449411983,"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."}}