{"id":"W3215146800","doi":"10.18280/ts.380520","title":"Design a Framework for Content Based Image Retrieval Using Hybrid Features Analysis","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metadata; Computer science; Field (mathematics); Matching (statistics); Image (mathematics); Heuristic; Image retrieval; Feature (linguistics); Data mining; Information retrieval; Content (measure theory); Pattern recognition (psychology); Artificial intelligence; Mathematics; Statistics","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":[0.001177424,0.0006862021,0.0008852097,0.002004369,0.0007344941,0.001665815,0.002650744,0.001419467,0.003929619],"category_scores_gemma":[0.001347335,0.0004928319,0.001644021,0.001661542,0.0009841326,0.002799685,0.00162486,0.0008308407,0.002924114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001336197,"about_ca_system_score_gemma":0.0009304295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005717536,"about_ca_topic_score_gemma":0.004405761,"domain_scores_codex":[0.9992096,0.0001486315,0.0000563549,0.0001788049,0.0003072002,0.00009932856],"domain_scores_gemma":[0.9996891,0.00006421322,0.00002851592,0.00006763278,0.0001264593,0.00002420974],"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":[0.0002374771,0.0003548668,0.001611044,0.0004783637,0.0002068608,0.0006497757,0.0007638368,0.1565245,0.06471906,0.3067999,0.01480365,0.4528507],"study_design_scores_gemma":[0.00004098958,0.0001458801,0.0002747904,0.00002589772,0.00005676091,0.0003062309,0.0001076134,0.9214999,0.009525492,0.04655774,0.02141128,0.00004741253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001097949,0.0001025195,0.9972983,0.00008985708,0.00001365172,0.0001056617,0.00003145701,0.0005001603,0.0007603512],"genre_scores_gemma":[0.07806215,0.0003209302,0.9165394,0.0001198431,0.0000636675,0.0004700555,0.0002614107,0.0001557433,0.004006673],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005717536,"threshold_uncertainty_score":0.01314586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08178321241025434,"score_gpt":0.3036277651510643,"score_spread":0.2218445527408099,"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."}}