{"id":"W2772698361","doi":"10.1109/pacrim.2017.8121890","title":"Automatic image cropping based on bottom-up saliency and top-down semantics","year":2017,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cropping; Image (mathematics); Computer science; Semantics (computer science); Top-down and bottom-up design; Computer vision; Artificial intelligence; Image resolution; Agricultural engineering; Engineering; Agriculture; Software engineering; Geography; Programming language","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":[0.0003561969,0.001068202,0.0008485812,0.001732178,0.0003536919,0.0006295393,0.0007627717,0.0004430884,0.002233174],"category_scores_gemma":[0.001504191,0.0003649973,0.0007749296,0.0008042307,0.0006142562,0.001251393,0.000850088,0.0006687345,0.0007167319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003132384,"about_ca_system_score_gemma":0.0004647672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001581029,"about_ca_topic_score_gemma":0.002292565,"domain_scores_codex":[0.9997293,0.0000326198,0.000009818012,0.00008371966,0.0001039085,0.00004060757],"domain_scores_gemma":[0.9993621,0.0001892107,0.00006269194,0.0001311981,0.0002018535,0.00005303928],"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":[0.0002396098,0.00008538412,0.0007482629,0.0002639589,0.00006877885,0.0002739958,0.0001621986,0.01189922,0.4793694,0.003190158,0.003842824,0.4998561],"study_design_scores_gemma":[0.00006104941,0.0003248978,0.007606371,0.0000384803,0.0001318477,0.001384103,0.0001598207,0.6113548,0.3569497,0.01175093,0.01014462,0.0000933409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03453141,0.0004979092,0.9600717,0.00009757876,0.00007812848,0.0001247759,0.00009556265,0.002708454,0.001794562],"genre_scores_gemma":[0.3468426,0.0006637779,0.6488981,0.00014063,0.0001269591,0.0001033325,0.000348821,0.0005357831,0.002340046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002233174,"threshold_uncertainty_score":0.007470727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922744407813361,"score_gpt":0.2974144239039,"score_spread":0.2781869798257664,"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."}}