{"id":"W4251040078","doi":"10.1109/icpr.2004.1334481","title":"Shape retrieval using concavity trees","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Silhouette; Pattern recognition (psychology); Computer science; Tree (set theory); Matching (statistics); Artificial intelligence; Image retrieval; Feature extraction; Feature (linguistics); Feature vector; Tree structure; Mathematics; Image (mathematics); Algorithm; Combinatorics; Binary tree; Statistics","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":[0.0004096501,0.0002869927,0.0002611467,0.0002377786,0.0001995474,0.0003043179,0.001854787,0.000147871,0.0002172204],"category_scores_gemma":[0.0002259171,0.0002322882,0.0001886339,0.0005188616,0.0002075534,0.0009142618,0.0002395014,0.0003396944,0.00008731696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003045573,"about_ca_system_score_gemma":0.0002176073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000745782,"about_ca_topic_score_gemma":0.000004313397,"domain_scores_codex":[0.9976087,0.00002074185,0.0005787383,0.0005537169,0.0009232723,0.0003148443],"domain_scores_gemma":[0.9972042,0.00003804801,0.0006186195,0.0002545945,0.001778511,0.0001060261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006003557,0.002795957,0.0225833,0.0003534972,0.0006246694,0.00002065496,0.002595545,0.00007822946,0.4768839,0.1683521,0.003929723,0.3211822],"study_design_scores_gemma":[0.001393891,0.000265986,0.00761206,0.0008357999,0.00004250765,0.00009085129,0.0001726308,0.01152964,0.8902648,0.08674274,0.0004319627,0.0006171059],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6855437,0.0001603508,0.2604093,0.0170559,0.00246088,0.001530114,0.0003201159,0.0008694442,0.03165014],"genre_scores_gemma":[0.993493,0.00005102365,0.005084869,0.0008378889,0.0001663202,0.00001150113,0.00001297971,0.00002096297,0.0003215202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.413381,"threshold_uncertainty_score":0.9472439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07459041303220466,"score_gpt":0.2986094031659968,"score_spread":0.2240189901337922,"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."}}