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I want to automate the classification of photographs by manufacturer - i.e. detect whether a photo is of a BMW or Porsche.

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itwasntandy/CarRecognition

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Car Recognition

Description

A tool which will analyse submitted images, and attempt to classify them through image recognition by manufacturer. It will be built on top of the OpenCV library.

Classifiers:

  • classifier by manufacturer
    • does it need to be by model type?
      • is generic Porsche (for example) classifier sufficient?
      • or do need 996, 997, Cayman, Boxster etc
    • separate classifiers for angle types (front, side, rear etc)
  • HAAR or LBP?
    • LBP potentially much quicker to train?
    • Which has greater accuracy for given training time?
      • (LBP could be trained with more images in same time?)
  • Accuracy?
    • how many training images are sufficient to get >70% accuracy
    • curve of accuracy… graph on time to train vs accuracy over different sized sets of prospects.
  • online training of classifiers?

Application:

  • take input files (image), add to queue.
    • Should it take input over HTTP or direct from disk?
    • initially HTTP I think, because not done it before.
  • have classifier workers pull images off the queue, attempt each form of classifier in turn (most populate types first)
  • worker output is:
    • If single classifier matched, HTTP response with classifier that matched.
    • if multiple classifiers match, then review confidence for each classifier
      • respond with both along with confidence for each.
    • if no classifiers match, then respond saying no match, flag for manual intervention.

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I want to automate the classification of photographs by manufacturer - i.e. detect whether a photo is of a BMW or Porsche.

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