{"id":"W39969610","doi":"10.2352/cgiv.2008.4.1.art00079","title":"Color Emotions for Image Classification and Retrieval","year":2008,"lang":"en","type":"article","venue":"Conference on Colour in Graphics Imaging and Vision","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Engineering Link (Canada)","funders":"","keywords":"Computer science; Image retrieval; Artificial intelligence; Color histogram; RGB color model; Color image; Pattern recognition (psychology); Content-based image retrieval; Histogram; Feature (linguistics); Visual Word; Image (mathematics); Feature extraction; Information retrieval; Computer vision; Image processing","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":[0.0007730165,0.0006695444,0.0007847325,0.001700841,0.0003468476,0.002137968,0.0007244342,0.0008117742,0.005057106],"category_scores_gemma":[0.002639458,0.0001655509,0.0005943321,0.002353561,0.0006437074,0.002208553,0.0006370886,0.0007516178,0.003125278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008999929,"about_ca_system_score_gemma":0.0003240634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001410778,"about_ca_topic_score_gemma":0.001071547,"domain_scores_codex":[0.9993256,0.00018345,0.0000537915,0.0001344041,0.0002474449,0.00005520247],"domain_scores_gemma":[0.9993043,0.0001900015,0.00006414457,0.0001639591,0.0002481384,0.00002954382],"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":[0.0002157679,0.00006512945,0.0008201956,0.000452256,0.00005788177,0.00009091444,0.0001481451,0.009091848,0.0347374,0.04351197,0.01963275,0.8911759],"study_design_scores_gemma":[0.00005891226,0.0002682801,0.007525517,0.000223424,0.0001741939,0.0008031472,0.0003967496,0.6677653,0.04580707,0.1603232,0.1165017,0.0001525634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0201583,0.02148764,0.9396605,0.001768372,0.0005075506,0.0002079608,0.0006828955,0.002634261,0.01289249],"genre_scores_gemma":[0.3276637,0.01300448,0.6441104,0.0005579445,0.0009394617,0.0004610909,0.001460819,0.0003669545,0.01143528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005057106,"threshold_uncertainty_score":0.01691765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05519990308156125,"score_gpt":0.3363256711611356,"score_spread":0.2811257680795743,"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."}}