{"id":"W1994312584","doi":"10.1016/j.image.2006.09.002","title":"Multi-object image retrieval based on shape and topology","year":2006,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Topology (electrical circuits); Object (grammar); Computer vision; Image (mathematics); Artificial intelligence; Mathematics; Combinatorics","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":[0.000630088,0.0001914266,0.0001803244,0.0001784418,0.0004768718,0.0004894896,0.0009842475,0.0001088789,0.00002675811],"category_scores_gemma":[0.00008415453,0.0001806163,0.00005023563,0.0005019308,0.0003554215,0.0008735561,0.0002213293,0.0003087067,0.00002650061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007131821,"about_ca_system_score_gemma":0.0001203368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003287871,"about_ca_topic_score_gemma":0.000002975896,"domain_scores_codex":[0.9984889,0.0002490522,0.0003369387,0.0004070535,0.0002663952,0.0002516579],"domain_scores_gemma":[0.9983868,0.0001813966,0.0002380566,0.0008098763,0.000327482,0.0000564167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001026594,0.0006499782,0.0002457436,0.0001379669,0.000006799853,0.00001056863,0.0002848887,0.000007977662,0.7911739,0.004200773,0.0006315284,0.2025473],"study_design_scores_gemma":[0.000588279,0.0001477575,0.004348043,0.0000984359,0.00001057025,0.00001420716,0.00002880267,0.7463173,0.2428444,0.004261294,0.00102825,0.000312625],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00165363,0.0008108217,0.9897648,0.00350326,0.00001963834,0.0002347003,0.000002975031,0.0005732938,0.003436852],"genre_scores_gemma":[0.6736208,0.00003097822,0.3257255,0.0003633107,0.00002407202,0.00001494622,0.00002223448,0.0000143354,0.0001838555],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7463093,"threshold_uncertainty_score":0.7365321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02156314554929563,"score_gpt":0.2863549870836185,"score_spread":0.2647918415343229,"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."}}