{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009294403,0.0007007865,0.000923273,0.00137512,0.0004510795,0.0009017555,0.001137339,0.001447631,0.003598166],"category_scores_gemma":[0.002364468,0.0003606652,0.0007925614,0.002062384,0.0006647966,0.001245235,0.001111854,0.001979104,0.00327386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004622,"about_ca_system_score_gemma":0.0007064992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001415416,"about_ca_topic_score_gemma":0.001557963,"domain_scores_codex":[0.9991778,0.0001570219,0.0000475874,0.0001714479,0.0003939804,0.00005203457],"domain_scores_gemma":[0.9994541,0.0001692945,0.00006571284,0.0001094642,0.0001752991,0.00002608395],"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":[0.0001207405,0.00007748455,0.0003516538,0.0001340941,0.00004550249,0.00007340218,0.000069665,0.0594187,0.02260049,0.04075009,0.009895376,0.8664628],"study_design_scores_gemma":[0.00002393437,0.00008858769,0.0003097197,0.00002757878,0.00001505137,0.0003195637,0.00001994755,0.9530632,0.009449732,0.0189158,0.01773734,0.00002961074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000749453,0.0001671597,0.9980927,0.00009912796,0.00003703594,0.00003042524,0.00003473724,0.0002418096,0.000547505],"genre_scores_gemma":[0.02882881,0.0005027981,0.9663259,0.0001518379,0.000123081,0.000152963,0.000301814,0.00004816435,0.003564526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003598166,"threshold_uncertainty_score":0.01203704,"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."}}