Row 75708

Row ID: 75708 | Dataset Entry | Axioma AXP Content Repository

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What do the images look like? I would normalize the mean to reduce exposure differences, convert to Lab color space, quantize the ab channels and cluster by that. Take the cluster representing the mode of chromacity and use it for class lookup or distance metric. If it's really mostly color differences then this should already work fairly well.

FieldValue
text What do the images look like? I would normalize the mean to reduce exposure differences, convert to Lab color space, quantize the ab channels and cluster by that. Take the cluster representing the mode of chromacity and use it for class lookup or distance metric. If it's really mostly color differences then this should already work fairly well.
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-24
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url_encoded Z0FBQUFBQm5Lak96LUlBT2xjcGpNc2hXRFRxeVVFaFJQMjBaWWFsNW9MZ21weVZaUFpGY3VGZHVWZk50NS1mSTB2TWJEM3d3S3pSaGpQTlJwSUowTjkyczhJUlRQUmxwUXhFQThSdkw5aDd0b01zclhZY3Vod1JuQ0dMNVN6MjVnMGpHS25ORF9mMEh1OXNVcjg3UHhzNUt0cnhrT1VKY2ExOG5iOWRqLU1kNFU5UGNvUGFSNDYxNm9WV2lfSUxtcktVQTVqb19vMV9yZkI1bGdHbFFwNFFkc3lZV3FGN19qelpiaVFmN002NmxoZ0RoRDFRalNLdz0=

Raw Record

{
  "text": "What do the images look like? I would normalize the mean to reduce exposure differences, convert to Lab color space, quantize the ab channels and cluster by that. Take the cluster representing the mode of chromacity and use it for class lookup or distance metric. If it's really mostly color differences then this should already work fairly well. ",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-24",
  "username_encoded": "Z0FBQUFBQm5Lak1pZVl5YVlvaC11czdIa1NGNC1oSS1yNzZoQW03REVYaEQtcjZ4Wk5RUVRCbkMxeHozSGswZkxncENTVEhiUjdadE9NVVRHLUN2V203T1dmS2JkX18xQVE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak96LUlBT2xjcGpNc2hXRFRxeVVFaFJQMjBaWWFsNW9MZ21weVZaUFpGY3VGZHVWZk50NS1mSTB2TWJEM3d3S3pSaGpQTlJwSUowTjkyczhJUlRQUmxwUXhFQThSdkw5aDd0b01zclhZY3Vod1JuQ0dMNVN6MjVnMGpHS25ORF9mMEh1OXNVcjg3UHhzNUt0cnhrT1VKY2ExOG5iOWRqLU1kNFU5UGNvUGFSNDYxNm9WV2lfSUxtcktVQTVqb19vMV9yZkI1bGdHbFFwNFFkc3lZV3FGN19qelpiaVFmN002NmxoZ0RoRDFRalNLdz0="
}
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