{"id":"W2014084502","doi":"10.1117/12.429520","title":"<title>Shape and spatial color information extraction for image retrieval</title>","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Search engine indexing; Computer vision; Image retrieval; Basis (linear algebra); Invariant (physics); Set (abstract data type); Information retrieval; Pattern recognition (psychology); Image (mathematics); 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009216852,0.0008453223,0.0009733679,0.001830846,0.0006754475,0.002657052,0.001526152,0.001149771,0.3460407],"category_scores_gemma":[0.002047407,0.0002683818,0.0003739628,0.002344593,0.0007447181,0.002355199,0.0009230764,0.0008293778,0.2706408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107669,"about_ca_system_score_gemma":0.0007139246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003366054,"about_ca_topic_score_gemma":0.004473074,"domain_scores_codex":[0.9993493,0.0001062479,0.00005286508,0.0001324419,0.0002902565,0.00006886541],"domain_scores_gemma":[0.9983674,0.000322882,0.00008336007,0.0002415057,0.0008345008,0.0001504478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003352864,0.0001010452,0.0004608004,0.0005569446,0.00001730499,0.0001906575,0.00006387316,0.0005058912,0.02674153,0.008065302,0.5526993,0.410262],"study_design_scores_gemma":[0.00005564528,0.0002747297,0.002098892,0.0001687736,0.00001873293,0.0006531358,0.00007368091,0.008191315,0.02570938,0.003015756,0.9596891,0.00005091128],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01913068,0.01648342,0.278083,0.01024715,0.02622328,0.002796635,0.008956727,0.02311121,0.6149679],"genre_scores_gemma":[0.04125119,0.007504333,0.07943975,0.001598067,0.003991491,0.0004129224,0.008408668,0.004012012,0.8533815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3460407,"threshold_uncertainty_score":0.9327936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01064064879849,"score_gpt":0.2375218239167273,"score_spread":0.2268811751182373,"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."}}