{"id":"W2296594867","doi":"10.1109/icip.2015.7351397","title":"A sparse coding method for semi-supervised segmentation with multi-class histogram constraints","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Histogram; Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer science; Image segmentation; Scale-space segmentation; Computer vision; Class (philosophy); 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":[0.0009802707,0.0006733564,0.001008074,0.0009573641,0.0003838222,0.0007386028,0.001476204,0.001214169,0.002162441],"category_scores_gemma":[0.002853955,0.0004995239,0.0007688884,0.001142756,0.0008788225,0.001341093,0.001088567,0.00169033,0.0008857765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005432902,"about_ca_system_score_gemma":0.001092476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002505195,"about_ca_topic_score_gemma":0.002967101,"domain_scores_codex":[0.9992705,0.0002125323,0.00003627936,0.0001557275,0.0002707949,0.00005410704],"domain_scores_gemma":[0.9987772,0.0005247946,0.0001312519,0.0001755541,0.0003337442,0.0000574777],"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":[0.0001563643,0.0001047444,0.0003175163,0.0002853841,0.00008368135,0.0001061553,0.0001970707,0.2996491,0.04849118,0.03184294,0.00676296,0.6120029],"study_design_scores_gemma":[0.000009050851,0.00003123555,0.00008458035,0.00001080498,0.000008009164,0.00006623693,0.000008789326,0.9886957,0.004698183,0.00464424,0.001730046,0.00001311793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006655979,0.00003895793,0.9989386,0.00002898069,0.00001014038,0.00001052275,0.00001258301,0.0001103029,0.0001842273],"genre_scores_gemma":[0.05464383,0.000189704,0.9428067,0.0001316207,0.00008293422,0.0001420475,0.0002403109,0.0001308578,0.001632036],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002505195,"threshold_uncertainty_score":0.007234037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08435190496585189,"score_gpt":0.3591541573013498,"score_spread":0.2748022523354979,"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."}}