{"id":"W2146543387","doi":"10.1109/icassp.2012.6288695","title":"A sparse reconstruction based algorithm for image and video classification","year":2012,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Pattern recognition (psychology); Sparse approximation; K-SVD; Class (philosophy); Dictionary learning; Facial recognition system; Noise (video); Contextual image classification; Image (mathematics); Face (sociological concept); Iterative reconstruction; Representation (politics)","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":[0.0000627227,0.00006113391,0.00005976998,0.00004392471,0.00002801623,0.00001876533,0.00002064449,0.00004157169,0.0000173213],"category_scores_gemma":[0.000006054462,0.00005882902,0.0000187908,0.00003518514,0.00001855286,0.0001599259,0.000004027425,0.00003271946,0.00000456399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001587307,"about_ca_system_score_gemma":0.000002607974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004390283,"about_ca_topic_score_gemma":9.632868e-7,"domain_scores_codex":[0.9997107,0.000005956449,0.00007539615,0.00006405106,0.00002977251,0.0001141399],"domain_scores_gemma":[0.9998032,0.00002814594,0.00001264235,0.00009174208,0.00002658503,0.00003772497],"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":[0.00000212503,0.000009075313,0.0001583405,0.00000859406,0.000008334109,1.282406e-7,0.00002004258,0.000006835837,0.05809249,0.0004222337,0.003924968,0.9373468],"study_design_scores_gemma":[0.0001402679,0.00001125276,0.001256112,0.00001401766,0.00001152614,0.00001443224,0.00003131438,0.849867,0.143358,0.0003907215,0.004804716,0.0001006051],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01994332,0.000121189,0.975593,0.0000394475,0.000193101,0.0001531754,0.000003591771,0.0005873435,0.003365813],"genre_scores_gemma":[0.4793238,0.00002145268,0.5204505,0.00003391047,0.00009884655,0.0000249488,0.000005232139,0.00001290467,0.00002838872],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9372462,"threshold_uncertainty_score":0.2398978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02767254617287604,"score_gpt":0.2456408480583634,"score_spread":0.2179683018854873,"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."}}