{"id":"W4249223683","doi":"10.32920/ryerson.14649465","title":"Combining visual features and contextual information for image retrieval and annotation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Image retrieval; Automatic image annotation; Artificial intelligence; Visual Word; Discriminative model; Annotation; Feature (linguistics); Pattern recognition (psychology); Semantic gap; Context (archaeology); Information retrieval; Visualization; Image (mathematics)","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":[0.001635819,0.001223318,0.001619859,0.00505917,0.0004968821,0.001846905,0.001440018,0.001406683,0.001910378],"category_scores_gemma":[0.004832633,0.0004445515,0.00189834,0.003650192,0.0009220936,0.003467488,0.001746676,0.001189657,0.001537224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007284165,"about_ca_system_score_gemma":0.0008752867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003625425,"about_ca_topic_score_gemma":0.004980594,"domain_scores_codex":[0.9979145,0.0004211459,0.0001273803,0.0006592742,0.0006383701,0.0002393495],"domain_scores_gemma":[0.9986147,0.0004077435,0.0001673615,0.0003803484,0.0003685321,0.00006133498],"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":[0.0004013273,0.0001891297,0.002332332,0.0004937891,0.0002099657,0.000151685,0.0001408973,0.02099035,0.04042451,0.007301019,0.006646265,0.9207187],"study_design_scores_gemma":[0.00005919774,0.0004838959,0.01172152,0.0002300493,0.0006441343,0.0007355178,0.0003217543,0.8641657,0.05495137,0.04003391,0.02644824,0.0002048155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02733641,0.004577447,0.9594912,0.0005608262,0.0001681836,0.0002455447,0.0007985025,0.002451276,0.0043707],"genre_scores_gemma":[0.4630146,0.004153886,0.5223622,0.0005759834,0.0006842497,0.0004826475,0.003565755,0.000408116,0.004752676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00505917,"threshold_uncertainty_score":0.008651137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01501415510110963,"score_gpt":0.2887571363566685,"score_spread":0.2737429812555588,"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."}}