{"id":"W2376819454","doi":"","title":"Image Retrieval Using SalientPoints and Geometric Hashing","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Geometric transformation; Computer science; Pattern recognition (psychology); Transformation geometry; Image retrieval; Wavelet transform; Invariant (physics); Computer vision; Hash table; Transformation (genetics); Matching (statistics); Content-based image retrieval; Hash function; Salient; Wavelet; Image (mathematics); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008245426,0.0006001029,0.001436086,0.003241733,0.0004888547,0.001026664,0.001323223,0.0009728803,0.002417789],"category_scores_gemma":[0.002545156,0.0004074445,0.000891314,0.002354029,0.0006554343,0.00295885,0.001250798,0.0004782001,0.001733456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005469047,"about_ca_system_score_gemma":0.0004424709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009400133,"about_ca_topic_score_gemma":0.0006625553,"domain_scores_codex":[0.9987382,0.0002012932,0.00006452853,0.0001976025,0.0007063065,0.0000920537],"domain_scores_gemma":[0.99931,0.0001719857,0.000113971,0.0001991691,0.0001757522,0.00002915466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003506837,0.0001379353,0.0008393437,0.0004938588,0.0001623126,0.0003820529,0.0001865629,0.02352693,0.1261327,0.02127811,0.006345882,0.8201636],"study_design_scores_gemma":[0.0002520897,0.001381971,0.005165584,0.00008823141,0.000261163,0.004610775,0.0002591677,0.7180378,0.1764787,0.04970763,0.04350885,0.0002479448],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01855944,0.002069463,0.9760192,0.0001267131,0.000123489,0.0001220702,0.00009158275,0.001207052,0.001680996],"genre_scores_gemma":[0.3097683,0.002027539,0.683014,0.0001791136,0.0005273512,0.0001668385,0.0005717088,0.0001570501,0.003588123],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003241733,"threshold_uncertainty_score":0.00808835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01134461478620031,"score_gpt":0.268361865113467,"score_spread":0.2570172503272667,"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."}}