{"id":"W2363876587","doi":"","title":"An Unsupervised Segmentation Framework for Texture Image Queries","year":2006,"lang":"en","type":"article","venue":"Computer Technology and Development","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":"Image texture; Computer science; Artificial intelligence; Texture compression; Texture (cosmology); Texture filtering; Pattern recognition (psychology); Image segmentation; Computer vision; Image (mathematics); Scale-space segmentation; Segmentation; Filter (signal processing); Feature (linguistics); Segmentation-based object categorization","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":[0.0006959049,0.0007946123,0.001029597,0.001510235,0.0005520504,0.001189671,0.001492597,0.001036084,0.002122251],"category_scores_gemma":[0.001894161,0.0003469301,0.0008096579,0.001567098,0.0007915288,0.002226546,0.0009645002,0.0008506869,0.0009148364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008205096,"about_ca_system_score_gemma":0.0008790817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004264252,"about_ca_topic_score_gemma":0.004980516,"domain_scores_codex":[0.9989535,0.0001853742,0.00006202121,0.0002564275,0.0004368099,0.000105839],"domain_scores_gemma":[0.9991518,0.0002352176,0.00008196052,0.0002076642,0.0002737614,0.00004968109],"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":[0.0005895174,0.0002836133,0.00132576,0.0003030422,0.00009907941,0.0004249472,0.0006027387,0.08182162,0.2359512,0.06482234,0.01744459,0.5963314],"study_design_scores_gemma":[0.00003980688,0.0001516613,0.0008881167,0.00000987259,0.0000261212,0.0004758263,0.000141889,0.9298588,0.03152419,0.02163594,0.0151957,0.00005194886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004582357,0.0001404423,0.9925351,0.00005514298,0.00001161517,0.00006211753,0.0001013601,0.001617523,0.0008942822],"genre_scores_gemma":[0.1727703,0.0003149436,0.8210125,0.0001450022,0.0001335375,0.0003274303,0.00105922,0.0004949612,0.003742179],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004264252,"threshold_uncertainty_score":0.00847888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028308459002408,"score_gpt":0.2595915980427059,"score_spread":0.2493085134526818,"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."}}