{"id":"W4250138412","doi":"10.1109/icpr.2004.1334418","title":"Combining visual features with semantics for a more effective image retrieval","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Image retrieval; Relevance feedback; Information retrieval; Semantics (computer science); Relevance (law); Visual Word; Content-based image retrieval; Image (mathematics); Preference; Artificial intelligence; Pattern recognition (psychology); 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":[0.001409472,0.001092214,0.001503998,0.002631853,0.0003375141,0.001735352,0.0008614989,0.001270282,0.002409864],"category_scores_gemma":[0.003530221,0.0004923923,0.001441363,0.002051888,0.0009166114,0.004661639,0.001186531,0.0009474597,0.001569298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003970113,"about_ca_system_score_gemma":0.0005631378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009008294,"about_ca_topic_score_gemma":0.001388691,"domain_scores_codex":[0.9992154,0.0002020862,0.00006408193,0.0001599925,0.0003041394,0.00005440192],"domain_scores_gemma":[0.9990005,0.0003972502,0.0001194591,0.0002330738,0.0002172371,0.00003249911],"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":[0.0002797843,0.0003895932,0.001375066,0.001263247,0.0002397601,0.0001936952,0.0001568684,0.01862729,0.2221197,0.01832859,0.005185293,0.7318411],"study_design_scores_gemma":[0.0002688279,0.001944066,0.006174671,0.0003171506,0.0006945608,0.002100381,0.0002700765,0.6575183,0.1445831,0.151011,0.03476994,0.0003478434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02643901,0.002496983,0.9664794,0.0005144638,0.0001208568,0.0001410986,0.0002094832,0.001723447,0.001875342],"genre_scores_gemma":[0.2688265,0.001356442,0.7265793,0.0003633939,0.0003419071,0.0001700559,0.0004797248,0.000210231,0.001672398],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002631853,"threshold_uncertainty_score":0.008061826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02330111645851784,"score_gpt":0.2920284911044203,"score_spread":0.2687273746459025,"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."}}