{"id":"W4382138553","doi":"10.1109/access.2023.3289759","title":"Reverse Image Search for Collage: A Novel Local Feature-Based Framework","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Scale-invariant feature transform; Computer science; Automatic summarization; Similarity (geometry); Image (mathematics); Process (computing); Image retrieval; Feature (linguistics); The Internet; Orb (optics); Artificial intelligence; Information retrieval; Pattern recognition (psychology); Computer vision; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005505501,0.0001753917,0.0002084351,0.0002156351,0.0002023196,0.0004508886,0.001789086,0.0001487843,0.000008050602],"category_scores_gemma":[0.0002575579,0.0001631478,0.0001166486,0.001941542,0.0001069164,0.001208921,0.0002946651,0.0002777181,0.00006475034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009383727,"about_ca_system_score_gemma":0.0001920269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002007613,"about_ca_topic_score_gemma":0.000003154001,"domain_scores_codex":[0.9983134,0.00003170524,0.0001733455,0.0005210727,0.0004612771,0.0004992365],"domain_scores_gemma":[0.9981452,0.0005386153,0.00006505531,0.0007342513,0.0003948886,0.0001219674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004713191,0.0004976487,0.0005135364,0.0007859757,0.00009009303,0.000468964,0.0006665017,0.002715363,0.09013488,0.03686303,0.5986217,0.268171],"study_design_scores_gemma":[0.0007901615,0.0001915085,0.0003637021,0.0001442554,0.00001003878,0.000006749627,0.00002270022,0.07524775,0.8648857,0.01742554,0.04046772,0.000444114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006898311,0.0000326959,0.9946342,0.002369942,0.0004116308,0.0005955729,0.00003114004,0.000989544,0.0002455176],"genre_scores_gemma":[0.2693751,0.00004195434,0.7253691,0.003679394,0.0003214541,0.0002539069,0.00001443261,0.00005451378,0.0008900761],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7747509,"threshold_uncertainty_score":0.6652974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06116822681460105,"score_gpt":0.3950994501512695,"score_spread":0.3339312233366684,"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."}}