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I need to visually recognise some flat pictures showed to camera. There are not many of them (maybe 30) but discrimination may depend on details. The input may be partly obscured or shadowed and is suspect to lighting changes. The samples need to be updatable.

There are many existing frameworks for object detection, with the most reliable ones depending on deep learning methods (mostly convolutional networks). However, the pretrained models are not well optimised to discern flat imagery of course, and even if I start training from scratch, updating the system for new samples would take a cumbersome training process, if I am right about how this works.

Is it possible to use deep learning while still keeping the sample pool flexible?

Is there any other well known reliable method to detect images from a small sample set?

dronus
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2 Answers2

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One can use well trained networks for visual classification like Inception or SqueezeNet, slice of the last layer(s) and add a simple statistical algorithm (for example k-nearest neighbour) that can be directly teached by the samples in a non-iterative fashion.

Most classification-related calculations like lighting and orientation insensitivity are already handled by the pre-trained network then, while the network's output keep enough information to allow statistical algorithms decide the image class.

An implementation using k-nearest neighbour is shown here: https://teachablemachine.withgoogle.com/ , the source is hosted here: https://github.com/googlecreativelab/teachable-machine .

dronus
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Use transfer learning; you’ll still need to build a training set, but you’ll get better results than starting with random weights. Try to find a model trained on images similar to yours. You might also do some black box testing of the selected model with your curated images to baseline it’s response curve to your images.

DCMartin
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  • This still needs a learning process to change the samples? I would like to keep samples as some kind of input that can be changed instantly. – dronus Aug 19 '18 at 13:43