{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002383186,0.0001270127,0.0001751751,0.0000770291,0.0001243572,0.00009411803,0.0004665805,0.00005009343,0.000006672213],"category_scores_gemma":[0.000065218,0.0000819156,0.0001002053,0.0001746756,0.0001096335,0.000166122,0.000229323,0.0001207351,0.00002176476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001636851,"about_ca_system_score_gemma":0.00002808271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008533891,"about_ca_topic_score_gemma":0.00001513169,"domain_scores_codex":[0.9991009,0.00008453637,0.0002680272,0.0002365719,0.0001282697,0.0001816629],"domain_scores_gemma":[0.998854,0.0003259033,0.0001679524,0.0003342331,0.0002701437,0.00004775189],"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":[0.000204607,0.0005972559,0.0001172797,0.0001589178,0.0001208733,0.00001072492,0.003384453,0.0001611197,0.01121325,0.04842245,0.02694158,0.9086675],"study_design_scores_gemma":[0.002129663,0.004019714,0.007912318,0.0007543948,0.00007925957,0.0002005402,0.0001632172,0.4220342,0.3635977,0.1960448,0.002017412,0.00104684],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06775444,0.00007504171,0.9305354,0.0005658788,0.0002864673,0.0005632993,0.000003932012,0.0001382145,0.00007736582],"genre_scores_gemma":[0.8910801,0.000008308976,0.1081195,0.000501078,0.000151031,0.00004844522,0.00000544703,0.0000100503,0.00007598649],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9076207,"threshold_uncertainty_score":0.3340421,"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."}}