{"id":"W3148394993","doi":"10.18280/mmep.080114","title":"Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features","year":2021,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Image retrieval; Artificial intelligence; Content-based image retrieval; Pattern recognition (psychology); Computer science; Wavelet transform; Precision and recall; Discrete wavelet transform; Feature (linguistics); Visual Word; Local binary patterns; Image texture; Wavelet; Feature vector; Computer vision; Image (mathematics); Image processing; Histogram","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002790143,0.0001554971,0.0002533327,0.00007460186,0.00005502013,0.0001016397,0.0001328866,0.0001019968,0.000003214864],"category_scores_gemma":[0.00003845068,0.0001231562,0.00005667147,0.0001923854,0.00003248477,0.00009187235,0.00002578586,0.0001689041,0.00000107571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001838672,"about_ca_system_score_gemma":0.0000248092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001394114,"about_ca_topic_score_gemma":3.481981e-8,"domain_scores_codex":[0.9990321,0.0000203023,0.0002680566,0.0002673326,0.0002393903,0.0001727524],"domain_scores_gemma":[0.9993672,0.0001489728,0.00004781736,0.0002439892,0.0001062004,0.00008582904],"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":[0.0001267308,0.0006190326,0.000007945046,0.02096001,0.00007519354,0.00007016196,0.001437982,0.07143538,0.4973535,0.3832639,0.00003104011,0.02461911],"study_design_scores_gemma":[0.0002259391,0.00006542975,0.000007468189,0.0008226075,0.000009592261,0.00001408268,0.00001641133,0.8484954,0.1493769,0.000824276,0.00002257055,0.000119384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004329132,0.0002775484,0.9942663,0.0005193804,0.00002543662,0.0001666642,0.000004919601,0.0002351947,0.0001753924],"genre_scores_gemma":[0.7209253,0.00002827454,0.2789325,0.0000316941,0.000009880017,0.000008918339,0.000003725409,0.00001330948,0.0000463779],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.77706,"threshold_uncertainty_score":0.5022166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285739851588513,"score_gpt":0.207722114940964,"score_spread":0.1848647164250788,"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."}}