{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002894177,0.000207648,0.0003198709,0.0004729338,0.0003379444,0.0001666667,0.0004640572,0.0002498255,0.000001624218],"category_scores_gemma":[0.00003688654,0.0001914971,0.0001370496,0.0005310446,0.0001546011,0.0004681514,0.000003991709,0.0002314081,0.00001164916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001608461,"about_ca_system_score_gemma":0.00009885846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002064333,"about_ca_topic_score_gemma":0.000006030436,"domain_scores_codex":[0.9985455,0.0000244987,0.0003870033,0.0005580912,0.0001467907,0.0003381466],"domain_scores_gemma":[0.9987447,0.000193062,0.0001639184,0.0004890401,0.0003348011,0.00007451434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002733199,0.0008421852,0.000006908018,0.0007315531,0.0003431108,0.00001855431,0.0008781691,0.0001356457,0.4209103,0.3173903,0.00064688,0.2578231],"study_design_scores_gemma":[0.001958431,0.001294668,0.00001256666,0.0001967029,0.00004377878,0.0002157811,0.0003651096,0.004811749,0.950754,0.02713189,0.01274854,0.0004668148],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002139364,0.0002225341,0.9925435,0.001990054,0.0007848537,0.001332515,0.00007368689,0.0008095689,0.0001039207],"genre_scores_gemma":[0.9904535,0.00007326334,0.008351986,0.0001247178,0.0000432813,0.0005253799,0.000002094671,0.00002563308,0.0004001227],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9883142,"threshold_uncertainty_score":0.7809024,"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."}}