{"id":"W2120486209","doi":"10.1109/tip.2005.863969","title":"Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Relevance feedback; Weighting; Probabilistic logic; Feature (linguistics); Feature selection; Image retrieval; Data mining; Relevance (law); Artificial intelligence; Pattern recognition (psychology); Context (archaeology); Similarity (geometry); Machine learning; Information retrieval; 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":[0.005083217,0.001728032,0.002269468,0.004007792,0.0009233174,0.001292447,0.003898715,0.002206479,0.002333246],"category_scores_gemma":[0.009492695,0.0006247825,0.001425855,0.002284395,0.001288984,0.002983727,0.001693669,0.001510352,0.001146734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062173,"about_ca_system_score_gemma":0.001255621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00393916,"about_ca_topic_score_gemma":0.003684942,"domain_scores_codex":[0.9957885,0.00128948,0.0002522979,0.0006826074,0.0018266,0.0001606599],"domain_scores_gemma":[0.9976047,0.001265249,0.0001648553,0.0002600686,0.0006444401,0.00006064104],"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":[0.0003395821,0.0002297903,0.0005609013,0.000552788,0.0001425459,0.0001410829,0.0002303678,0.02496301,0.02341215,0.01284485,0.006510066,0.9300728],"study_design_scores_gemma":[0.0002015812,0.0005867216,0.001628515,0.0001357336,0.0002628641,0.000934631,0.0001055048,0.9002643,0.02054863,0.05168173,0.0234847,0.0001650647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003693199,0.001321799,0.9927555,0.0002048116,0.00009378508,0.0002243011,0.00004943508,0.0008644572,0.0007927267],"genre_scores_gemma":[0.156622,0.000947465,0.8376068,0.0004175511,0.0004405771,0.0006375586,0.000213565,0.000214369,0.002900092],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005083217,"threshold_uncertainty_score":0.02688295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01508547826667142,"score_gpt":0.2412298420891336,"score_spread":0.2261443638224622,"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."}}