{"id":"W2132480052","doi":"10.1109/ideas.2006.50","title":"Visual Keyword-based Image Retrieval using Latent Semantic Indexing, Correlation-enhanced Similarity Matching and Query Expansion in Inverted Index","year":2006,"lang":"en","type":"article","venue":"Proceedings - International Database Engineering and Applications Symposium","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Inverted index; Codebook; Computer science; Pattern recognition (psychology); Visual Word; Search engine indexing; Artificial intelligence; Image retrieval; Feature vector; 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":[0.0005014074,0.0004511553,0.001152667,0.001843732,0.0003529701,0.0008817204,0.001288561,0.0005252758,0.001250638],"category_scores_gemma":[0.001583371,0.0002283592,0.0006057428,0.002641329,0.0004855928,0.00210064,0.0008649267,0.0003803462,0.0008705839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000617396,"about_ca_system_score_gemma":0.0007261342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003347104,"about_ca_topic_score_gemma":0.00441231,"domain_scores_codex":[0.9993727,0.000103839,0.00004479276,0.00009588112,0.0003290018,0.0000537968],"domain_scores_gemma":[0.9995401,0.0001094652,0.00007038727,0.0001061238,0.0001478113,0.0000260576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004254569,0.0003364139,0.001215878,0.0004426039,0.00009744233,0.0002712349,0.0002430778,0.03229934,0.1683894,0.01559204,0.007645344,0.7730418],"study_design_scores_gemma":[0.0001358017,0.0004974778,0.002260652,0.00003127353,0.0001128742,0.0009574958,0.0002218532,0.8744321,0.09410765,0.01728699,0.009834462,0.0001212728],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03306265,0.001151464,0.9612275,0.00013209,0.00005552376,0.0001930156,0.0002708931,0.001932379,0.001974557],"genre_scores_gemma":[0.2655533,0.0006720067,0.7299078,0.00009988763,0.0001141288,0.000268922,0.0009638259,0.0001139936,0.002306218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003347104,"threshold_uncertainty_score":0.006655216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007686563174386889,"score_gpt":0.2406206691568438,"score_spread":0.2329341059824569,"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."}}