{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003195223,0.0001255663,0.0001437297,0.00007567709,0.00005841978,0.0001437424,0.0004190151,0.0001109115,0.00003112517],"category_scores_gemma":[0.0002985073,0.0001075756,0.0001684446,0.0002072476,0.00007919799,0.0008065155,0.0000770725,0.0001104131,0.0000107561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007191183,"about_ca_system_score_gemma":0.00002412321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001426909,"about_ca_topic_score_gemma":3.170222e-8,"domain_scores_codex":[0.9990544,1.363586e-8,0.0003074243,0.000163977,0.0003133501,0.0001608674],"domain_scores_gemma":[0.9987162,0.00006110466,0.0002016441,0.00004134684,0.000928051,0.00005165897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004296587,0.00003678797,0.00001269808,0.000163546,0.00005620745,4.428286e-8,0.00006721917,7.60334e-7,0.388804,0.5885046,0.01128486,0.0110263],"study_design_scores_gemma":[0.0008924358,0.0003980663,0.0007372028,0.0001574761,0.00007998933,0.00004459403,0.0001352354,0.3082148,0.3100019,0.006115503,0.372773,0.0004497884],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8829099,0.0001959471,0.06752063,0.005893565,0.0007500826,0.00126592,0.00006039439,0.0004291996,0.04097438],"genre_scores_gemma":[0.2796156,0.0009344579,0.714195,0.0004173888,0.0010735,0.0002401115,0.00003875436,0.00007623895,0.003408944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6466743,"threshold_uncertainty_score":0.4386806,"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."}}