{"id":"W3113501994","doi":"10.1016/j.cviu.2022.103415","title":"A non-alternating graph hashing algorithm for large-scale image search","year":2022,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Hash function; Computer science; Computational complexity theory; Locality-sensitive hashing; Binary code; Algorithm; Binary number; Relaxation (psychology); Coordinate descent; Mathematical optimization; Theoretical computer science; Mathematics; Hash table","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":[0.0008569591,0.0007782399,0.001302172,0.001055557,0.0006407208,0.000826937,0.002320173,0.001180279,0.00416045],"category_scores_gemma":[0.003030624,0.0004546792,0.0007198886,0.001868804,0.0007641172,0.002344311,0.001617571,0.001379277,0.002041442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000647195,"about_ca_system_score_gemma":0.001671533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003515594,"about_ca_topic_score_gemma":0.004391594,"domain_scores_codex":[0.9991978,0.0002027879,0.00004815542,0.0001664644,0.0003148981,0.00006989067],"domain_scores_gemma":[0.999154,0.0002817879,0.00009719168,0.0002139056,0.0001954255,0.0000576726],"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":[0.0002826467,0.0002100534,0.0007250263,0.0003298454,0.0001067805,0.0001271236,0.000167878,0.2951229,0.0134994,0.05512384,0.02256342,0.6117411],"study_design_scores_gemma":[0.00003662433,0.00005683972,0.0001395867,0.000008344087,0.000009073062,0.000106292,0.00002474477,0.9747804,0.00155269,0.02049518,0.002771682,0.00001858857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005519723,0.0005201235,0.9913464,0.0001822452,0.00006339154,0.0000792587,0.00008387137,0.001012995,0.001191926],"genre_scores_gemma":[0.1609433,0.0005370929,0.8322582,0.0003291773,0.0001436966,0.0002949563,0.0007658585,0.0002319648,0.004495728],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00416045,"threshold_uncertainty_score":0.01391816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441565489459691,"score_gpt":0.3253175015625593,"score_spread":0.2909018466679624,"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."}}