{"id":"W2038846353","doi":"10.1145/2499788.2499822","title":"Personalized image retrieval in compressed domain based on user interest model","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Program for New Century Excellent Talents in University; Research Grants Council, University Grants Committee; National Natural Science Foundation of China; Canadian Institute for Advanced Research","keywords":"Computer science; Image retrieval; JPEG; Visual Word; Computer vision; Process (computing); Artificial intelligence; Information retrieval; Precision and recall; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000627965,0.0005222435,0.0008545174,0.0009359947,0.0002707237,0.0006458455,0.0006763132,0.000622197,0.001238785],"category_scores_gemma":[0.00226819,0.0001998566,0.0005946109,0.0008745255,0.0003263723,0.001636845,0.0004513859,0.0005323883,0.0006452454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004354466,"about_ca_system_score_gemma":0.0004046587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002925304,"about_ca_topic_score_gemma":0.002450198,"domain_scores_codex":[0.9990671,0.0002222169,0.00004736654,0.0001673475,0.0004192363,0.00007670441],"domain_scores_gemma":[0.9991981,0.0002750557,0.00006186519,0.0001806433,0.0002540615,0.00003028434],"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":[0.001188041,0.0005068196,0.004558137,0.0003606521,0.0001925204,0.0008467484,0.0004495923,0.0816648,0.1516152,0.01687256,0.009422058,0.732323],"study_design_scores_gemma":[0.0000509949,0.0003034413,0.002593654,0.00001181589,0.0001019288,0.001361346,0.00009018926,0.9514264,0.03678813,0.003328098,0.003883648,0.00006031874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07135166,0.001360221,0.9214306,0.0002797291,0.00006874985,0.0002329633,0.0001504679,0.001297633,0.003828065],"genre_scores_gemma":[0.7904004,0.001661474,0.1997924,0.000263657,0.0002211097,0.0001907698,0.0006248613,0.00007887819,0.006766477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002925304,"threshold_uncertainty_score":0.005816519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02771807493508988,"score_gpt":0.2879143588270577,"score_spread":0.2601962838919678,"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."}}