{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001385699,0.0006060174,0.0014791,0.001695368,0.0004473734,0.001092509,0.0009642104,0.00104413,0.001758886],"category_scores_gemma":[0.002893531,0.0004847065,0.001101963,0.001416217,0.0006397308,0.001690217,0.0009294022,0.000873875,0.0008697726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007277133,"about_ca_system_score_gemma":0.0007005752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338983,"about_ca_topic_score_gemma":0.001220196,"domain_scores_codex":[0.9985505,0.0002028267,0.00008986468,0.0002905987,0.0007231315,0.0001431124],"domain_scores_gemma":[0.9988716,0.0002744641,0.00009790518,0.0001802089,0.0005338969,0.00004188529],"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":[0.000331092,0.0000916287,0.0009943267,0.0001777577,0.00008950117,0.0001122381,0.0001334123,0.05171785,0.1874506,0.007625298,0.001643635,0.7496327],"study_design_scores_gemma":[0.00002652178,0.0002389112,0.001976674,0.00001945411,0.00007360201,0.0002651039,0.00004198386,0.8918527,0.09311208,0.007350333,0.00498967,0.00005282489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007296258,0.00009769614,0.9914545,0.00003739417,0.00002729501,0.00003719727,0.00001762514,0.0005427928,0.000489337],"genre_scores_gemma":[0.209243,0.0001659572,0.7885451,0.00006319891,0.00007472988,0.0001336054,0.0001437506,0.0001164196,0.001514199],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001758886,"threshold_uncertainty_score":0.007328391,"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."}}