{"id":"W2066804252","doi":"10.1109/icassp.2013.6637991","title":"Image similarity measurement from sparse reconstruction errors","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Qatar National Research Fund","keywords":"Artificial intelligence; Computer science; Similarity (geometry); Pattern recognition (psychology); Image (mathematics); Benchmark (surveying); Similarity measure; Cluster analysis; Measure (data warehouse); Set (abstract data type); Computer vision; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002154933,0.00008971109,0.00008940896,0.00004620291,0.00007662991,0.0001975248,0.0004051197,0.00005018161,0.0006509945],"category_scores_gemma":[0.00004930613,0.00007319044,0.00004654342,0.0001765442,0.0000463104,0.0009470236,0.00008654211,0.00008978898,0.0005795124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000710358,"about_ca_system_score_gemma":0.00003885104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005106048,"about_ca_topic_score_gemma":0.0000119971,"domain_scores_codex":[0.9990345,0.00004634596,0.0001842957,0.0002740454,0.0003167202,0.0001440664],"domain_scores_gemma":[0.9990978,0.00001570932,0.00006614072,0.0004404294,0.0003076641,0.0000722734],"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":[0.000002658687,0.0001118886,0.0009715941,0.00000773736,0.00002328541,0.00000196757,0.0001484137,1.354672e-7,0.2863777,0.01113481,0.01425649,0.6869634],"study_design_scores_gemma":[0.0002717682,0.00005676712,0.03495892,0.00002532924,0.000008576467,0.00001180281,0.0001045807,0.05849835,0.8218638,0.07825942,0.005532293,0.0004083797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004182589,0.0000270473,0.9787825,0.003093811,0.0002033564,0.0001956856,0.00000108057,0.0005620094,0.01295191],"genre_scores_gemma":[0.4852554,0.00001894629,0.5138754,0.0004365517,0.00004585976,0.00003781519,0.000001726633,0.000005279178,0.0003230508],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.686555,"threshold_uncertainty_score":0.7448652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04103006070839316,"score_gpt":0.2388728941095706,"score_spread":0.1978428334011774,"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."}}