{"id":"W1980549145","doi":"10.1109/icra.2012.6224741","title":"Indexing visual features: Real-time loop closure detection using a tree structure","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Search engine indexing; Artificial intelligence; Tree structure; Key frame; Pattern recognition (psychology); Scalability; Computer vision; Tree (set theory); Feature extraction; Feature (linguistics); Frame (networking); Data structure; Mathematics; Database","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":[0.0001969656,0.0001856568,0.0001712119,0.0001419233,0.0001773429,0.0001264764,0.0003446726,0.0001561391,0.00003741485],"category_scores_gemma":[0.00005898102,0.0001530574,0.00007108569,0.0005601013,0.00003083787,0.001878431,0.0002020285,0.0002327339,0.00001324171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001003087,"about_ca_system_score_gemma":0.00003391042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005281876,"about_ca_topic_score_gemma":0.00001097784,"domain_scores_codex":[0.9987341,0.00006051149,0.0001793531,0.0002889081,0.0002656006,0.0004715298],"domain_scores_gemma":[0.9992834,0.00005169012,0.00009722482,0.000349386,0.00008058795,0.0001376784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002195455,0.00004882872,0.001577181,0.00001701548,0.00002149149,0.000007543742,0.0003973632,0.00001448137,0.695506,0.002051856,0.0001868399,0.3001494],"study_design_scores_gemma":[0.0002227587,0.0001384302,0.004051057,0.0000290231,0.00001371689,0.0001473701,0.00003325747,0.01178012,0.9796456,0.002665296,0.0008993999,0.0003739735],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1306428,0.0001284495,0.8666242,0.00002473774,0.0001727619,0.0001477537,0.000001062371,0.0006632825,0.001594915],"genre_scores_gemma":[0.8351135,0.00001777745,0.163917,0.0001392581,0.0002396532,0.00000220945,0.000001801691,0.00001686954,0.0005518498],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7044708,"threshold_uncertainty_score":0.6241501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01440977865803935,"score_gpt":0.3012373194852013,"score_spread":0.286827540827162,"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."}}