{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002391324,0.0002099629,0.0002401493,0.0002142982,0.00004575156,0.0002246382,0.0008818893,0.0000815373,0.0002065501],"category_scores_gemma":[0.00008671249,0.0001718931,0.0000882757,0.0004629722,0.00009211271,0.001175855,0.0001994507,0.0002576581,0.0001570308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008474369,"about_ca_system_score_gemma":0.00005329157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004413953,"about_ca_topic_score_gemma":0.000005187272,"domain_scores_codex":[0.9984953,0.00008699528,0.0002864838,0.0004825468,0.0002864288,0.0003622884],"domain_scores_gemma":[0.9988706,0.0001577138,0.0000677585,0.0006691246,0.0001198332,0.0001150324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000759037,0.001763309,0.0009223304,0.000134863,0.00003490453,0.0002395823,0.001761499,0.002205207,0.5838521,0.3528894,0.03460464,0.02083312],"study_design_scores_gemma":[0.001166147,0.0001652772,0.000384595,0.00005890986,0.000001331224,0.000002221874,0.00002882031,0.8475114,0.1163543,0.03281954,0.001193452,0.0003139283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01624155,0.00001599823,0.9743286,0.001574258,0.00004052848,0.00043334,0.000002153064,0.0004238188,0.006939713],"genre_scores_gemma":[0.4737164,0.000005558712,0.5226061,0.002376931,0.00001448332,0.00002543068,0.000002816745,0.0000155339,0.001236726],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8453062,"threshold_uncertainty_score":0.7009598,"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."}}