{"id":"W1991990099","doi":"10.1109/globalsip.2014.7032285","title":"Generalized Gaussian mixture Conditional Random Field model for image labeling","year":2014,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gaussian; Conditional random field; Feature (linguistics); Laplace operator; Pattern recognition (psychology); Mixture model; Computer science; Inference; Gradient descent; Artificial intelligence; Gaussian random field; Image (mathematics); Laplacian matrix; Blob detection; Algorithm; Gaussian process; Mathematics; Image processing; Artificial neural network; Physics; Edge detection","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":[0.002377469,0.001088937,0.001525418,0.00186642,0.0005975022,0.001164634,0.003524977,0.002037436,0.003118416],"category_scores_gemma":[0.005317272,0.000682103,0.001769242,0.002455761,0.0013541,0.002711298,0.001174191,0.002192999,0.001186264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001990631,"about_ca_system_score_gemma":0.00145261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009956662,"about_ca_topic_score_gemma":0.008786058,"domain_scores_codex":[0.9984583,0.0005996142,0.00005211794,0.0004118473,0.000339943,0.0001382462],"domain_scores_gemma":[0.9983851,0.0008934413,0.0001683364,0.0002114395,0.0002796343,0.00006209455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001229865,0.00004623606,0.0007939117,0.0001276403,0.0000716211,0.0001328503,0.0001250556,0.7865629,0.002449049,0.1225045,0.005045608,0.08201762],"study_design_scores_gemma":[0.000003931122,0.000007834793,0.00008530699,0.000005791555,0.000006753711,0.00002642052,0.000004190348,0.975715,0.0002740871,0.02301602,0.0008435206,0.00001121749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002026979,0.0002161228,0.996534,0.0001689449,0.00002547349,0.000022293,0.0001006016,0.0003365121,0.0005690739],"genre_scores_gemma":[0.3661003,0.001023162,0.6223452,0.0004944726,0.0002148044,0.0005427843,0.001476936,0.0004370153,0.007365194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009956662,"threshold_uncertainty_score":0.01979738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01755998443677346,"score_gpt":0.2748795305025628,"score_spread":0.2573195460657893,"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."}}