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This is a fairly straightforward question, but I am new to the field. Using this tutorial I have a great way of detecting certain patterns or features. However, the images I'm testing are large and often the feature I'm looking for only occupies a small fraction of the image. When I run it on the entire picture the classification is bad, though when zoomed it and cropped the classification is good.

I've considered writing a script that breaks an image into many different images and runs the test on all (time isn't a huge concern). However, this still seems inefficient and unideal. I'm wondering about suggestions for the best, but also easiest to implement, solution for this.

I'm using Python.

Community
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user7299543
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1 Answers1

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This may seem to be a simple question, which it is, but the answer is not so simple. Localisation is a difficult task and requires much more leg work than classifying an entire image. There are a number of different tools and models that people have experimented with. Some models include R-CNN which looks at many regions in a manner not too dissimilar to what you suggested. Alternatively you could look at a model such as YOLO or TensorBox.

There is no one answer to this, and this gets asked a lot! For example: Does Convolutional Neural Network possess localization abilities on images?

The term you want to be looking for in research papers is "Localization". If you are looking for a dirty solution (that's not time sensitive) then sliding windows is definitely a first step. I hope that this gets you going in your project and you can progress from there.

JCooke
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