{"id":"W2132783522","doi":"10.1109/icme.2009.5202623","title":"Multimodal image retrieval via bayesian information fusion","year":2009,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Maximum a posteriori estimation; Relevance feedback; Image retrieval; Pattern recognition (psychology); A priori and a posteriori; Visual Word; Domain (mathematical analysis); Bayes' theorem; Probabilistic logic; Feature (linguistics); Bayesian probability; Naive Bayes classifier; Class (philosophy); Image (mathematics); Mathematics; Support vector machine; Statistics","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":[0.002667722,0.0009027851,0.00196031,0.002634905,0.000616494,0.001659483,0.001700365,0.001436718,0.002070876],"category_scores_gemma":[0.006307648,0.0005445695,0.001193276,0.001843744,0.0008677358,0.003573131,0.001776227,0.001154423,0.001222523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001324511,"about_ca_system_score_gemma":0.0008456728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003357459,"about_ca_topic_score_gemma":0.002612932,"domain_scores_codex":[0.9975782,0.0006375557,0.0001458703,0.0003529627,0.001136115,0.0001491773],"domain_scores_gemma":[0.9988824,0.0004548678,0.0001370931,0.0001376444,0.0003502365,0.00003790859],"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":[0.0003982375,0.0001775842,0.0007377887,0.0004664664,0.0002322188,0.0002413807,0.000333721,0.1354753,0.0411076,0.03868189,0.005865133,0.7762825],"study_design_scores_gemma":[0.00004875181,0.0001520928,0.0006625147,0.00004868485,0.0001076929,0.000281572,0.00006484547,0.9384692,0.01236471,0.04282124,0.004864912,0.0001138064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004691726,0.001376383,0.9917086,0.0002301718,0.0000311946,0.00005857371,0.0000535732,0.0004504306,0.001399336],"genre_scores_gemma":[0.4427112,0.00266832,0.5490825,0.000520931,0.0003707033,0.0002937902,0.0005436711,0.0001345859,0.003674303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003357459,"threshold_uncertainty_score":0.01410842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006509477188644206,"score_gpt":0.2388854189628949,"score_spread":0.2323759417742507,"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."}}