{"id":"W2093952231","doi":"10.1109/mmsp.2010.5662071","title":"A hierarchical statistical model for object classification","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Latent Dirichlet allocation; Contextual image classification; Artificial intelligence; Object (grammar); Generative model; Hierarchical database model; Image (mathematics); Pattern recognition (psychology); Bag-of-words model in computer vision; Data modeling; Dirichlet distribution; Machine learning; Generative grammar; Data mining; Topic model; Image retrieval; Visual Word; Database; 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":[0.003387727,0.0009228697,0.001289614,0.00249678,0.0006597702,0.001562235,0.003094492,0.001575892,0.003423049],"category_scores_gemma":[0.006898815,0.0006717219,0.002011588,0.003348464,0.001406734,0.002775024,0.001273379,0.002160224,0.003131845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001774686,"about_ca_system_score_gemma":0.001385863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009402817,"about_ca_topic_score_gemma":0.01147197,"domain_scores_codex":[0.9974629,0.0008383068,0.0001419777,0.000539096,0.000839481,0.0001782412],"domain_scores_gemma":[0.9977441,0.001291044,0.0001768581,0.0004169463,0.0003098101,0.00006121065],"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":[0.0001899944,0.0002030177,0.002777884,0.0003878966,0.0002818738,0.0002203142,0.0004102759,0.3230661,0.007469073,0.2423449,0.0133604,0.4092883],"study_design_scores_gemma":[0.00001152178,0.00004494121,0.0005103083,0.00001833014,0.00002867314,0.00008048749,0.00001595623,0.9091615,0.0006037322,0.08586329,0.003637539,0.00002359763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002892249,0.0005245796,0.9945412,0.000214937,0.00005161904,0.00005588635,0.0002439885,0.0006287533,0.0008467974],"genre_scores_gemma":[0.3362603,0.001774993,0.6450412,0.0006684837,0.000590114,0.0009133399,0.002566457,0.0003482084,0.01183691],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009402817,"threshold_uncertainty_score":0.01869619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0373962252261471,"score_gpt":0.3121882926860683,"score_spread":0.2747920674599212,"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."}}