{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001046134,0.0001386524,0.0001331292,0.0002437449,0.000247309,0.0001365491,0.0004232538,0.0001938815,0.000003504762],"category_scores_gemma":[0.00000640597,0.0001249149,0.00001784441,0.0003426177,0.0001071314,0.0003357386,0.0001259166,0.0001095688,0.000007118245],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003167902,"about_ca_system_score_gemma":0.00005867597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001363038,"about_ca_topic_score_gemma":0.000001205172,"domain_scores_codex":[0.9991034,0.00001627971,0.0002169334,0.0003728535,0.00009227928,0.000198231],"domain_scores_gemma":[0.9994438,0.00003652922,0.00006869389,0.0003031254,0.0001147252,0.00003314908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005827544,0.00008187056,0.00105441,0.00002497261,0.000009208728,0.000002984312,0.0001990086,7.934635e-7,0.004722798,0.4669302,0.0004045231,0.5265634],"study_design_scores_gemma":[0.0006542398,0.0003206048,0.01335386,0.00006086293,0.000006920398,0.00004572447,0.0001464291,0.02016778,0.5094259,0.4189677,0.0362048,0.0006452006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005000332,0.0001610709,0.9918748,0.001618998,0.0001065512,0.0002829173,0.000001365383,0.0009189086,0.00003501371],"genre_scores_gemma":[0.1410104,0.000009541501,0.8585351,0.0002424481,0.00003668107,0.0001055149,0.00001841418,0.000006657145,0.00003516412],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5259182,"threshold_uncertainty_score":0.5093881,"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."}}