{"id":"W1976860949","doi":"10.1117/12.503262","title":"<title>Content-based image retrieval using a Gaussian mixture model in the wavelet domain</title>","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000928188,0.0005529425,0.0008843792,0.001401262,0.0003075955,0.001102566,0.001015673,0.0009681236,0.005509519],"category_scores_gemma":[0.001400568,0.0003044455,0.0007198498,0.001797293,0.0006256173,0.001485159,0.0004376837,0.0006527058,0.007030768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005950207,"about_ca_system_score_gemma":0.0004348928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003559709,"about_ca_topic_score_gemma":0.003223327,"domain_scores_codex":[0.9995658,0.00007815018,0.00002383973,0.00009380472,0.0002034211,0.0000350059],"domain_scores_gemma":[0.9995307,0.0001052659,0.00003978558,0.0001175081,0.0001880215,0.00001874135],"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":[0.0004026507,0.0001492512,0.001307799,0.0002755686,0.00008542989,0.0003853198,0.0001015286,0.03938124,0.1380517,0.02543311,0.02855598,0.7658705],"study_design_scores_gemma":[0.00003507642,0.000152466,0.002864841,0.00003830002,0.00004688223,0.0006320013,0.00003600761,0.8842214,0.06980953,0.009518893,0.03255917,0.00008542218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01056275,0.0009577828,0.9812113,0.0004780862,0.0001787747,0.00007924846,0.0002778469,0.002455111,0.003799186],"genre_scores_gemma":[0.246589,0.002754504,0.7124718,0.0004452379,0.0003173302,0.000183392,0.002585353,0.0007841417,0.03386925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005509519,"threshold_uncertainty_score":0.01843119,"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."}}