{"id":"W2168616308","doi":"10.1109/tkde.2008.34","title":"Automatic Website Summarization by Image Content: A Case Study with Logo and Trademark Images","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Killam Trusts","keywords":"Automatic summarization; Computer science; Trademark; Logo (programming language); Information retrieval; Logos Bible Software; World Wide Web; Web page; Image (mathematics); Trademark infringement; Artificial intelligence; Process (computing); Abstraction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001443547,0.0005666213,0.0006172368,0.003131361,0.0009629243,0.001246418,0.00113948,0.001374009,0.001672545],"category_scores_gemma":[0.006546338,0.0002729683,0.0005608222,0.002936864,0.0005240938,0.001143919,0.0005874162,0.0005156256,0.0007203206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000567614,"about_ca_system_score_gemma":0.0003488674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004044503,"about_ca_topic_score_gemma":0.01051175,"domain_scores_codex":[0.9988409,0.0004396498,0.00009980626,0.0001937912,0.0003588815,0.00006706653],"domain_scores_gemma":[0.9903235,0.006623314,0.0006734622,0.0007935975,0.001289323,0.0002967251],"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":[0.001495567,0.003789277,0.05063954,0.003664483,0.0002681775,0.02419809,0.01153718,0.02892784,0.09994285,0.001994503,0.01622092,0.7573217],"study_design_scores_gemma":[0.0005415774,0.003635154,0.170125,0.000395344,0.0006951624,0.02581302,0.01990877,0.4619428,0.2278323,0.005177205,0.08354817,0.0003855907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9178832,0.00086388,0.07249583,0.0006287985,0.0000516162,0.0009689569,0.001373036,0.001724949,0.004009716],"genre_scores_gemma":[0.7815812,0.0005796135,0.2096185,0.0001484914,0.00008503195,0.0002197935,0.002592474,0.0003402803,0.004834668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004044503,"threshold_uncertainty_score":0.008041918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03497416171297994,"score_gpt":0.2577001686089124,"score_spread":0.2227260068959325,"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."}}