{"id":"W2105799736","doi":"10.1109/cvpr.2005.340","title":"The Distinctiveness, Detectability, and Robustness of Local Image Features","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Robustness (evolution); Optimal distinctiveness theory; Scale-invariant feature transform; Classifier (UML); Feature extraction; Contextual image classification; Scalability; Linear discriminant analysis; 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":[0.001640099,0.0007342772,0.001070722,0.00205442,0.0002656836,0.001134175,0.001006166,0.0007501674,0.001544905],"category_scores_gemma":[0.008520946,0.0003773624,0.0009958053,0.001014453,0.001489559,0.002452871,0.000962943,0.001068692,0.0007808573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004873696,"about_ca_system_score_gemma":0.0003134808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006376663,"about_ca_topic_score_gemma":0.0005307004,"domain_scores_codex":[0.9986629,0.000157368,0.00008537275,0.0004277069,0.0005696689,0.00009696279],"domain_scores_gemma":[0.9950583,0.002157398,0.0009203551,0.001013149,0.0006669011,0.0001838736],"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":[0.000365222,0.0001920839,0.01148519,0.0003964466,0.0002340486,0.0004579281,0.0001719248,0.05280209,0.3602868,0.01337491,0.001573092,0.5586603],"study_design_scores_gemma":[0.0000603129,0.0009878477,0.04023293,0.00005852515,0.0002606942,0.003640011,0.0001213023,0.6781843,0.2424867,0.02683553,0.006909065,0.0002228087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06901328,0.0007858791,0.9267116,0.0001392459,0.00005303148,0.0000782811,0.0001762656,0.0008593883,0.002183069],"genre_scores_gemma":[0.763495,0.0006815377,0.2326884,0.0001021144,0.0002524306,0.0001578285,0.0004593372,0.0002294474,0.001933859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00205442,"threshold_uncertainty_score":0.008673787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005831756936450689,"score_gpt":0.2576683004804668,"score_spread":0.2518365435440161,"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."}}