{"id":"W2362607289","doi":"","title":"Image Texture Segmentation Based on Feature Fusion and Classifier Fusion","year":2006,"lang":"en","type":"article","venue":"Computer Engineering and Applications Journal","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Gabor filter; Classifier (UML); Computer science; Segmentation; Fusion; Image texture; Support vector machine; Discrete cosine transform; Computer vision; Image segmentation; Feature extraction; 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":[0.000135721,0.0001294548,0.00009443673,0.0001535015,0.0002362667,0.0003730538,0.0001827045,0.00006927279,0.000002969811],"category_scores_gemma":[0.00000252299,0.0001093573,0.00003243294,0.0002337991,0.00002345813,0.0002215739,0.00005723162,0.0002432884,0.000003068111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003203211,"about_ca_system_score_gemma":0.00001597831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001098645,"about_ca_topic_score_gemma":1.039518e-7,"domain_scores_codex":[0.9993089,0.00001811147,0.0001511138,0.0002274982,0.0001606107,0.0001336954],"domain_scores_gemma":[0.9995241,0.00004373331,0.00007549995,0.0001966646,0.00008064547,0.00007938706],"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":[0.000009563422,0.0001720416,0.0001899129,0.0000799566,0.00001079519,0.00001134589,0.00008062698,0.002146059,0.1071338,0.01438204,0.006278786,0.869505],"study_design_scores_gemma":[0.0003171418,0.00006703325,0.007128953,0.00005269959,0.000006819354,0.0001272723,0.000003804548,0.9535862,0.00683212,0.0007329819,0.03095707,0.0001879092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001114126,0.0002189145,0.9966515,0.001468265,0.00006783948,0.0001532738,0.000001986422,0.0001786072,0.0001455027],"genre_scores_gemma":[0.2505371,0.0001757715,0.7479456,0.00033699,0.0007210393,0.00005623615,0.00001983191,0.00001986507,0.0001875705],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9514402,"threshold_uncertainty_score":0.4459462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005166368904703065,"score_gpt":0.2135758346534769,"score_spread":0.2084094657487738,"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."}}