{"id":"W2146744268","doi":"10.1109/icdim.2008.4746716","title":"Adaptive mean shift-based image segmentation using multiple instance learning","year":2008,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Cluster analysis; Segmentation; Artificial intelligence; Segmentation-based object categorization; Image segmentation; Mean-shift; Scale-space segmentation; Relevance feedback; Image retrieval; Object (grammar); Pattern recognition (psychology); Context (archaeology); Computer vision; Relevance (law); Image (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":[0.001311657,0.0013093,0.00213495,0.001585006,0.0005229404,0.00142841,0.003437684,0.002258557,0.001964901],"category_scores_gemma":[0.00301142,0.0007860478,0.001707576,0.001688644,0.0009039277,0.00200602,0.001234553,0.001916402,0.001123501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138143,"about_ca_system_score_gemma":0.0008860869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003914798,"about_ca_topic_score_gemma":0.004276255,"domain_scores_codex":[0.9989466,0.0002105547,0.00005560027,0.0004246176,0.0002603936,0.000102239],"domain_scores_gemma":[0.9990696,0.0003144522,0.000130442,0.0002236012,0.0002076513,0.00005426886],"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":[0.0003673328,0.0002604602,0.001020334,0.0001727387,0.0002472804,0.0001093225,0.000134346,0.237893,0.05729243,0.003970521,0.004017368,0.6945149],"study_design_scores_gemma":[0.0000134986,0.00005323807,0.0003166728,0.000005194287,0.00002282165,0.00006618448,0.00001298213,0.9826931,0.013092,0.003062124,0.0006465915,0.00001566446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009124244,0.0001677559,0.9881604,0.00006491721,0.00002292956,0.00006115996,0.00004321599,0.001950562,0.0004048413],"genre_scores_gemma":[0.1889657,0.0002111796,0.8079411,0.0001675902,0.00007568789,0.0001776542,0.0004668767,0.0004049866,0.0015892],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003914798,"threshold_uncertainty_score":0.008257806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04840474979055058,"score_gpt":0.2705062909380631,"score_spread":0.2221015411475125,"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."}}