I'm trying to detect the visual attention area in a given image and crop the image into that area. For instance, given an image of any size and a rectangle of say LxW dimension as an input, I would like to crop the image to the most important visual attention area. I'm looking for a state of the art approach for that.

Do we have any tools or SDK to implement that? Any piece of code or algorithm would really help.

  • BTW, within a "single" object, I would like to get attention. So object detection might not be the best thing. – Tina J Jun 18 at 14:06
  • Are you looking for deep learning based approaches or classical image processing based approaches? – varsh Jun 25 at 6:39
  • Any of them that would work better. Deep Learning might be a better choice. – Tina J Jun 25 at 16:51

You can search for the following paper titles:

  1. A Deep Multi-Level Network for Saliency Prediction.
  2. Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN.

You can code in python using Pytorch framework.

"Attention" in neural network (visual) is the area of the image where the network can find most number of features to classify it with high confidence.Based on your description you are talking about "soft attention".

Do we have any tools or SDK to implement that? i don't think there are readymade SDKs available. It is much better to train a model on your dataset with attention. Once you have your base model ready , it is easy to add attention mechanism for it.I suggest you to check https://arxiv.org/pdf/1502.03044.pdf.

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