{"id":"W2520081143","doi":"10.1007/978-3-319-46487-9_5","title":"Evaluation of LBP and Deep Texture Descriptors with a New Robustness Benchmark","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Local binary patterns; Computer science; Robustness (evolution); Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Computational complexity theory; Benchmark (surveying); Pooling; Feature extraction; Deep learning; Machine learning; Histogram; Image (mathematics); Algorithm","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":[0.002275241,0.001653003,0.001223629,0.002585406,0.0003809043,0.00108315,0.001332462,0.001172629,0.00376904],"category_scores_gemma":[0.007017079,0.0002625175,0.0008274257,0.001309001,0.0005221216,0.001124659,0.001298702,0.000675156,0.001310885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005077544,"about_ca_system_score_gemma":0.0006361912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005616237,"about_ca_topic_score_gemma":0.004686131,"domain_scores_codex":[0.9979341,0.0002812488,0.0001855195,0.000357622,0.001041769,0.0001997823],"domain_scores_gemma":[0.996283,0.001282698,0.0002733805,0.0006504751,0.001272589,0.0002379047],"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":[0.007346561,0.001954455,0.009167242,0.001485762,0.0009275133,0.0003840808,0.00006310968,0.149733,0.118942,0.001523292,0.02168947,0.6867834],"study_design_scores_gemma":[0.0004974108,0.004169958,0.0194037,0.00009578868,0.0004653879,0.0007443327,0.0001160939,0.8817478,0.08564392,0.001270895,0.005751466,0.0000933495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7467747,0.009454765,0.2126911,0.0005972445,0.001227065,0.0008751816,0.008499837,0.007798367,0.01208171],"genre_scores_gemma":[0.875201,0.001323988,0.09474307,0.0002424394,0.0002071384,0.0002228434,0.02003623,0.0004976754,0.007525702],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.005616237,"threshold_uncertainty_score":0.01260871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02368547676231646,"score_gpt":0.2704795486172638,"score_spread":0.2467940718549473,"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."}}