{"id":"W2037823779","doi":"10.1109/tip.2011.2170701","title":"Wavelet Modeling Using Finite Mixtures of Generalized Gaussian Distributions: Application to Texture Discrimination and Retrieval","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Wavelet; Histogram; Pattern recognition (psychology); Artificial intelligence; Gaussian; Image texture; Mathematics; Probability distribution; Kullback–Leibler divergence; Wavelet transform; Statistical model; Marginal distribution; Probability density function; Computer science; Algorithm; Image processing; Statistics; Random variable; 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":[0.0002226723,0.0001738133,0.0001787522,0.0002494321,0.0003366898,0.0001297868,0.0002930537,0.00009681617,0.000006116064],"category_scores_gemma":[0.00002136357,0.0001613638,0.00005967914,0.0007525646,0.00007682619,0.0008524465,0.000006658158,0.0001723989,0.0000025778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006607407,"about_ca_system_score_gemma":0.00007460628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004612469,"about_ca_topic_score_gemma":0.000002602706,"domain_scores_codex":[0.9987262,0.00005246214,0.0003621607,0.0004068676,0.0002511968,0.0002010629],"domain_scores_gemma":[0.9990988,0.00002565903,0.0001525025,0.0003169101,0.0003099152,0.00009624344],"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":[0.0001238828,0.0002918972,0.000004316256,0.0002101146,0.00001971592,0.000002209606,0.002953182,0.0007662338,0.5696341,0.001733676,0.000005452722,0.4242552],"study_design_scores_gemma":[0.0001218841,0.00003911259,0.00002323403,0.00007165098,0.00002220076,0.000007727409,0.00004461273,0.5718923,0.425668,0.001968425,0.00000794271,0.0001328502],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003975836,0.0001059117,0.9950209,0.0002331136,0.00005428678,0.0003021663,0.00002368856,0.0001887917,0.0000952608],"genre_scores_gemma":[0.7525224,0.00002730054,0.247319,0.0000572795,0.00001419935,0.00002305881,0.000004088815,0.00001167259,0.00002096619],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7485466,"threshold_uncertainty_score":0.6580227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03821652583416461,"score_gpt":0.2839654373276539,"score_spread":0.2457489114934893,"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."}}