{"id":"W1999403468","doi":"10.1007/s00371-013-0813-5","title":"Spatial consistency of dense features within interest regions for efficient landmark recognition","year":2013,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Landmark; Artificial intelligence; Discriminative model; Computer science; Pattern recognition (psychology); Salient; Scale-invariant feature transform; Histogram; Feature (linguistics); Matching (statistics); Computer vision; Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007980932,0.0004582367,0.0009632211,0.001820401,0.0003776483,0.001017975,0.001413171,0.0006582983,0.001916648],"category_scores_gemma":[0.005048108,0.0006319288,0.0005681631,0.001908512,0.0006337442,0.001466524,0.00139456,0.0008768113,0.0008864352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004713983,"about_ca_system_score_gemma":0.001144433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004124372,"about_ca_topic_score_gemma":0.0069877,"domain_scores_codex":[0.9992455,0.0001178477,0.00004447041,0.0001830934,0.0002970193,0.0001121128],"domain_scores_gemma":[0.9983807,0.0005268715,0.0002241409,0.0004574735,0.0003540292,0.00005686007],"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":[0.0007643444,0.0002281796,0.005622697,0.0002648773,0.0001440869,0.0002308949,0.0002401158,0.1166565,0.1293489,0.01940556,0.007180922,0.7199129],"study_design_scores_gemma":[0.00004250479,0.0001145957,0.004119448,0.0000208137,0.00004910187,0.0003444706,0.00009084722,0.9422272,0.03967134,0.01010905,0.003182438,0.0000281084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02609044,0.0002004166,0.972291,0.0000624639,0.00002085233,0.00003977341,0.0001605051,0.0006534643,0.0004810057],"genre_scores_gemma":[0.57234,0.000402865,0.4240786,0.00008979755,0.00007930829,0.0001274271,0.001133901,0.0003750593,0.001373105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004124372,"threshold_uncertainty_score":0.008200705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04169819558974866,"score_gpt":0.3092225057880488,"score_spread":0.2675243101983001,"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."}}