{"id":"W2307700635","doi":"10.1109/ism.2015.36","title":"Normalized Gaussian Distance Graph Cuts for Image Segmentation","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Image segmentation; Cut; Segmentation; Artificial intelligence; Spectral clustering; Computer science; Gaussian; Cluster analysis; Graph; Pattern recognition (psychology); Kernel (algebra); Algorithm; Mathematics; Computer vision; Theoretical computer science; Combinatorics","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":[0.001216036,0.001379297,0.001420055,0.003281114,0.0007386185,0.001692788,0.002347402,0.001938643,0.002441274],"category_scores_gemma":[0.004660716,0.0009243102,0.001295605,0.003967613,0.00153321,0.002750001,0.001112639,0.002058264,0.001424466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001843772,"about_ca_system_score_gemma":0.00137341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005941498,"about_ca_topic_score_gemma":0.005907593,"domain_scores_codex":[0.9978628,0.000469851,0.00009484187,0.0005688641,0.0008826348,0.0001210977],"domain_scores_gemma":[0.9983021,0.0006403321,0.0001737679,0.000237206,0.0005862566,0.00006040398],"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":[0.0002660583,0.00006638859,0.0005627391,0.0004637713,0.0001412335,0.0001725122,0.0002805642,0.2765906,0.03667848,0.07128396,0.008367379,0.6051263],"study_design_scores_gemma":[0.00001820506,0.00006415054,0.0004249921,0.00003747053,0.00003588067,0.0002709995,0.00005414886,0.92451,0.01835711,0.0445963,0.01157487,0.00005589251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001089003,0.0002381776,0.9977844,0.00004128692,0.00002664228,0.00002220547,0.00002950643,0.0004065047,0.0003622261],"genre_scores_gemma":[0.05631602,0.0006485376,0.940389,0.00009497024,0.0000780486,0.0001184124,0.0003032701,0.0004251222,0.001626525],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005941498,"threshold_uncertainty_score":0.01337749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03020819431277451,"score_gpt":0.3259625799918429,"score_spread":0.2957543856790684,"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."}}