END-TO-END SALIENCY MAPPING VIA PROBABILITY DISTRIBUTION PREDICTION
First Claim
1. A method for generating a system for predicting saliency in an image, comprising:
- for each of a set of training images;
generating an attention map; and
representing the attention map as a first probability distribution which includes, for each of a set of pixels, a respective value corresponding to a probability of the pixel being fixated upon; and
with a processor, training a neural network to output a saliency map for an input image, the training including updating parameters of the neural network to optimize an objective function over the training set, the objective function being based on a distance measure computed between a second probability distribution computed for a saliency map output by the neural network, given an input training image, and the first probability distribution computed for the attention map of the respective training image, the second probability distribution including, for each of the set of pixels, a respective probability.
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Abstract
A method for generating a system for predicting saliency in an image and method of use of the prediction system are described. Attention maps for each of a set of training images are used to train the system. The training includes passing the training images though a neural network and optimizing an objective function over the training set which is based on a distance measure computed between a first probability distribution computed for a saliency map output by the neural network and a second probability distribution computed for the attention map for the respective training image. The trained neural network is suited to generation of saliency maps for new images.
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Citations
20 Claims
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1. A method for generating a system for predicting saliency in an image, comprising:
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for each of a set of training images; generating an attention map; and representing the attention map as a first probability distribution which includes, for each of a set of pixels, a respective value corresponding to a probability of the pixel being fixated upon; and with a processor, training a neural network to output a saliency map for an input image, the training including updating parameters of the neural network to optimize an objective function over the training set, the objective function being based on a distance measure computed between a second probability distribution computed for a saliency map output by the neural network, given an input training image, and the first probability distribution computed for the attention map of the respective training image, the second probability distribution including, for each of the set of pixels, a respective probability. - View Dependent Claims (2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15)
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8. (canceled)
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16. A training system for generating a prediction system for predicting saliency in an image, comprising:
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memory which stores an attention map for each of a set of training images, the attention map having been generated based on eye gaze data, the attention map being modeled as a first probability distribution which includes, for each of a set of pixels, a respective value corresponding to a probability of the pixel being fixated upon; a training component which trains a neural network with the training images and the attention maps by optimizing an objective function over the training set, the objective function being based on a distance measure computed between a second probability distribution computed for a saliency map output by the neural network, when input with a training image, and the first probability distribution for the respective training image; and a hardware processor which implements the training component. - View Dependent Claims (17)
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18. A method for predicting saliency in an image, comprising:
providing a trained neural network trained by a method comprising; generating an attention map for each of a set of training images, modeling each attention map as a first probability distribution which includes, for each of a set of pixels, a respective value corresponding to a probability of the pixel being fixated upon, and training a neural network to output a saliency map for an input image, the training comprising updating parameters of the neural network to optimize an objective function over the training set, which is based on a distance measure computed between the first probability distribution for the attention map and a second probability distribution for a saliency map output by the neural network for the respective training image, the second probability distribution including, for each of the set of pixels, a respective probability; receiving an image; and passing the image through the neural network to generate a saliency map for the image. - View Dependent Claims (19, 20)
Specification