{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002066308,0.0004511171,0.0003828463,0.0009395795,0.0002163179,0.0004891052,0.0006576471,0.0003301713,0.01845296],"category_scores_gemma":[0.00165642,0.0001262614,0.0002735166,0.001366947,0.00031244,0.0007868055,0.0005855257,0.0005390933,0.004460282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003566278,"about_ca_system_score_gemma":0.0001801386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006045751,"about_ca_topic_score_gemma":0.0008329299,"domain_scores_codex":[0.9997612,0.0000302938,0.00001500603,0.0000293425,0.0001494586,0.00001470476],"domain_scores_gemma":[0.999469,0.0002339726,0.0000474346,0.0001166751,0.0001115546,0.00002139738],"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":[0.0003031048,0.00004274791,0.0003292726,0.0005071066,0.00006008064,0.0002986669,0.00009353667,0.1237199,0.03430108,0.02885302,0.04004008,0.7714515],"study_design_scores_gemma":[0.00002911405,0.0001886663,0.0008130398,0.0001278993,0.00003669251,0.000788765,0.00003390183,0.8931366,0.02250838,0.02799259,0.05431838,0.00002593109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02713055,0.002830506,0.9520019,0.0005882703,0.0005108258,0.000103497,0.0009057563,0.002751902,0.01317675],"genre_scores_gemma":[0.309301,0.00376314,0.6488528,0.0002572963,0.0006304647,0.0003221016,0.003375238,0.0006250351,0.03287293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01845296,"threshold_uncertainty_score":0.06173134,"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."}}