{"id":"W1581044618","doi":"10.1007/3-540-45925-1_6","title":"Region-Based Image Retrieval Using Multiple-Features","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Image retrieval; Segmentation; Computer vision; Invariant (physics); Pattern recognition (psychology); Translation (biology); Computation; Image segmentation; Rotation (mathematics); Representation (politics); Image (mathematics); Mathematics; Algorithm","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":[0.0005468123,0.0008478154,0.002136455,0.002128358,0.0003762695,0.001020651,0.001553292,0.0009655599,0.004263246],"category_scores_gemma":[0.001108449,0.0004914946,0.001158333,0.00287913,0.0003286673,0.002172538,0.0009728256,0.0004783103,0.003617624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003608614,"about_ca_system_score_gemma":0.0002932364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340089,"about_ca_topic_score_gemma":0.001846508,"domain_scores_codex":[0.9994686,0.00006556537,0.00003823453,0.0001038958,0.0002642189,0.00005939397],"domain_scores_gemma":[0.9995609,0.0001261822,0.00004352239,0.0001137111,0.0001376148,0.00001802908],"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":[0.0004473911,0.000114705,0.0002855806,0.000388383,0.0001482569,0.0002061674,0.00003914084,0.007710089,0.3040948,0.00165154,0.005239286,0.6796747],"study_design_scores_gemma":[0.0001092329,0.0006558307,0.003825131,0.00005145654,0.0005041306,0.003212814,0.00007400576,0.5471256,0.4215908,0.004870354,0.01786562,0.0001149704],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02265977,0.004169047,0.9674293,0.00008041715,0.0001544185,0.000128249,0.0002193803,0.003107472,0.002051988],"genre_scores_gemma":[0.1731045,0.00268329,0.8139799,0.0001328631,0.0002064587,0.0001635086,0.00103116,0.0004362252,0.008262046],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004263246,"threshold_uncertainty_score":0.01426202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03231419870604681,"score_gpt":0.2625422292725524,"score_spread":0.2302280305665056,"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."}}