{"id":"W1976860949","doi":"10.1117/12.503262","title":"&lt;title&gt;Content-based image retrieval using a Gaussian mixture model in the wavelet domain&lt;/title&gt;","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wavelet; Pattern recognition (psychology); Artificial intelligence; Content-based image retrieval; Computer science; Image retrieval; Feature extraction; Subspace topology; Feature (linguistics); Wavelet transform; Feature vector; Search engine indexing; Linear subspace; Mixture model; Computer vision; Mathematics; 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":[0.0006249249,0.0001792217,0.0001925035,0.00008446596,0.00005953802,0.0001337512,0.0009488797,0.0001309604,0.00001957505],"category_scores_gemma":[0.0002608384,0.0001288638,0.0002859193,0.0004050824,0.0001245004,0.0003184017,0.00006538656,0.0002365884,0.000005621906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001162269,"about_ca_system_score_gemma":0.00006188001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001100207,"about_ca_topic_score_gemma":3.276044e-8,"domain_scores_codex":[0.998629,6.159826e-8,0.0003481613,0.0002592063,0.0005141043,0.0002494955],"domain_scores_gemma":[0.9990613,0.00005551864,0.0001708029,0.00008395711,0.000575825,0.00005252603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008625077,0.00003787891,0.000007902253,0.0000612916,0.00002399426,1.408114e-7,0.00006686644,0.000005135567,0.38662,0.6116966,0.001362334,0.0001092162],"study_design_scores_gemma":[0.001096365,0.0001901681,0.0001843085,0.0003339781,0.00007601331,0.00004051967,0.0004308471,0.382242,0.5731397,0.01702061,0.02460671,0.000638773],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9149762,0.0002119924,0.04034844,0.004894393,0.0002932194,0.0007501766,0.00003187961,0.0001918487,0.03830189],"genre_scores_gemma":[0.137273,0.0000508145,0.8613633,0.0003613242,0.0001689927,0.00003266644,0.000003954849,0.00004330323,0.0007026403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8210149,"threshold_uncertainty_score":0.5254915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207025342104922,"score_gpt":0.2403671829197251,"score_spread":0.2196646487092329,"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."}}