{"id":"W2125018361","doi":"10.1109/ccece.2003.1226103","title":"A statistical approach for image feature extraction in the wavelet domain","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Feature extraction; Image retrieval; Computer science; Artificial intelligence; Wavelet; Feature (linguistics); Search engine indexing; Content-based image retrieval; Wavelet transform; Feature vector; Feature detection (computer vision); Image (mathematics); Image processing","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":[0.0003999998,0.00006438555,0.00006275682,0.00004004919,0.00006516335,0.0001381201,0.0003815747,0.00004723169,0.000003921686],"category_scores_gemma":[0.00003702531,0.00003879702,0.00002825952,0.0002355645,0.00003515009,0.0002618889,0.00002291319,0.000118805,0.000005774403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004150474,"about_ca_system_score_gemma":0.00003657027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001240846,"about_ca_topic_score_gemma":0.000002165727,"domain_scores_codex":[0.9993791,0.00003744361,0.00009808259,0.0001936997,0.0001520002,0.0001396671],"domain_scores_gemma":[0.9995922,0.00008089244,0.00002826875,0.0002396782,0.00003811037,0.00002086668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007330298,0.0001381981,0.00000331221,0.00001198138,0.000001584947,0.000004970467,0.000323895,7.433138e-7,0.004453616,0.9725943,0.002526493,0.01993355],"study_design_scores_gemma":[0.002298164,0.0004583404,0.01016326,0.00002050294,0.00001127825,0.0003222597,0.001313062,0.03946555,0.09431594,0.8109492,0.04002663,0.0006558118],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009448748,0.00001070191,0.9877158,0.005467636,0.00002056824,0.0003494129,0.000003800385,0.0001041256,0.006233402],"genre_scores_gemma":[0.09323253,0.000003784082,0.9059432,0.0004744678,0.00002637083,0.00009634894,0.00001093389,0.000003099004,0.0002092866],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1616451,"threshold_uncertainty_score":0.1582097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01829990078851727,"score_gpt":0.2931535029673153,"score_spread":0.274853602178798,"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."}}