{"id":"W1716119910","doi":"10.1109/icdsp.1997.628028","title":"Directional detail histogram for content based image retrieval","year":2002,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Histogram; Computer science; Search engine indexing; Image histogram; Image texture; Image retrieval; Artificial intelligence; Computer vision; Content-based image retrieval; Image (mathematics); Histogram matching; Randomness; Pattern recognition (psychology); Information retrieval; Image processing; Mathematics","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":[0.0004547464,0.0003084674,0.0005391131,0.002137557,0.0002232878,0.0009412675,0.0005836522,0.0004888188,0.004993463],"category_scores_gemma":[0.001905529,0.0001621399,0.0002872753,0.003071316,0.0003347081,0.001156345,0.000408406,0.0004884481,0.003184829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005308201,"about_ca_system_score_gemma":0.0003683357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001947003,"about_ca_topic_score_gemma":0.001535598,"domain_scores_codex":[0.9996476,0.00005194037,0.00001879429,0.00004505927,0.0002050103,0.00003155686],"domain_scores_gemma":[0.9994605,0.0001513484,0.00004226101,0.0001707088,0.0001532308,0.00002187105],"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":[0.0002426253,0.00008462814,0.0009005668,0.0003747119,0.0000519468,0.000123839,0.00007112363,0.02277358,0.08118578,0.05287812,0.0213731,0.81994],"study_design_scores_gemma":[0.00007499413,0.0001952267,0.006412757,0.00007677461,0.0001089669,0.001276563,0.00009399789,0.74759,0.0706415,0.05346414,0.1199415,0.0001234511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009851868,0.00445726,0.9752194,0.0002577626,0.0001701589,0.0001408754,0.0006949088,0.003770807,0.005436966],"genre_scores_gemma":[0.2077218,0.004514658,0.7752987,0.000311389,0.0003835993,0.0002488274,0.002264915,0.0003859616,0.00887027],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004993463,"threshold_uncertainty_score":0.0167048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08432482724436725,"score_gpt":0.2578779528205696,"score_spread":0.1735531255762024,"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."}}