{"id":"W2107930181","doi":"10.1109/tcsvt.2004.826759","title":"Query Feedback for Interactive Image Retrieval","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Information retrieval; Relevance feedback; Image retrieval; Search engine indexing; Visual Word; Session (web analytics); Content-based image retrieval; Similarity (geometry); Query expansion; Artificial intelligence; 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":[0.001304822,0.000852589,0.000885661,0.0007053508,0.0005547506,0.0007772683,0.001755181,0.001323346,0.0085335],"category_scores_gemma":[0.006519907,0.0003177565,0.0003955199,0.0005882944,0.0005560383,0.001763417,0.001147284,0.0007973729,0.002364899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006951602,"about_ca_system_score_gemma":0.0005908565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004044125,"about_ca_topic_score_gemma":0.004159383,"domain_scores_codex":[0.998516,0.0004508837,0.00006035858,0.0001941303,0.0006736592,0.0001049772],"domain_scores_gemma":[0.9978638,0.001226878,0.00008643353,0.0002899011,0.0004587393,0.00007421889],"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":[0.001974549,0.0005003836,0.0008595785,0.0003616368,0.00006572105,0.0004473004,0.0005388357,0.04355602,0.1599763,0.01350365,0.01462176,0.7635942],"study_design_scores_gemma":[0.0001364625,0.0003671541,0.0007909901,0.00001695362,0.00003902278,0.0005498988,0.0001253584,0.9232364,0.05285797,0.009068483,0.0127709,0.00004048589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01710623,0.0007231768,0.9746055,0.0002064858,0.00005192063,0.0001548877,0.00006783905,0.004568962,0.002515],"genre_scores_gemma":[0.4343956,0.0005173857,0.5562406,0.0002513131,0.0001384962,0.0003366443,0.0004557195,0.0004284125,0.007235835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0085335,"threshold_uncertainty_score":0.02854741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0215831930276203,"score_gpt":0.2708843779486944,"score_spread":0.2493011849210741,"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."}}