{"id":"W2547637848","doi":"10.1145/2988272.2988286","title":"Evaluation of the Power Consumption of Image Descriptors on Smartphone Platforms","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"AdaBoost; Computer science; Artificial intelligence; Support vector machine; Energy consumption; Object detection; Energy (signal processing); Detector; Pattern recognition (psychology); Haar-like features; Statistical classification; Machine learning; Computer vision; Face detection; Engineering; Mathematics; Facial recognition system","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":[0.00050219,0.0008576522,0.0006630257,0.001463424,0.0002389505,0.0006106633,0.0007988026,0.0003821261,0.002281398],"category_scores_gemma":[0.003140263,0.0001848843,0.0002938241,0.001679979,0.0002043483,0.0007793765,0.0004044491,0.0002171159,0.000545281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005352448,"about_ca_system_score_gemma":0.0003184249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003630192,"about_ca_topic_score_gemma":0.00425095,"domain_scores_codex":[0.9990243,0.0001375008,0.00009153398,0.000183803,0.0004104773,0.0001523792],"domain_scores_gemma":[0.9983667,0.0007403104,0.0001465765,0.00018459,0.0004822463,0.00007957273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00595418,0.0008864399,0.04448586,0.00202207,0.0005927543,0.001623728,0.0003793797,0.08807729,0.1334126,0.001088828,0.01828876,0.7031882],"study_design_scores_gemma":[0.0004413824,0.005870334,0.1520968,0.0001401517,0.0006348537,0.00251438,0.001110496,0.6034539,0.2151225,0.001046465,0.01738144,0.0001873308],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824316,0.001613908,0.008813083,0.0001664031,0.00009073761,0.0001108845,0.001256914,0.00189428,0.003622141],"genre_scores_gemma":[0.9890671,0.0004822587,0.006328975,0.00005878577,0.00001760543,0.0000771817,0.001540987,0.0001216805,0.002305522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003630192,"threshold_uncertainty_score":0.007632017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04810791933898153,"score_gpt":0.3092268768788355,"score_spread":0.261118957539854,"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."}}