Food logging from images
First Claim
1. A computer-implemented process for evaluating images of meals, comprising:
- using a computer to perform process actions for;
receiving a meal image of a single meal consisting of multiple food items;
wherein one or more food items are visible and one or more of the food items are occluded in the meal image;
receiving information indicating a source of the meal;
recognizing one or more of the visible food items and one or more of the occluded food items in the meal by evaluating the meal image using a machine-learned meal model that is constrained by the source of the meal; and
presenting one or more interactive messages automatically generated based on the recognized food items.
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Abstract
A “Food Logger” provides various approaches for learning or training one or more image-based models (referred to herein as “meal models”) of nutritional content of meals. This training is based on one or more datasets of images of meals in combination with “meal features” that describe various parameters of the meal. Examples of meal features include, but are not limited to, food type, meal contents, portion size, nutritional content (e.g., calories, vitamins, minerals, carbohydrates, protein, salt, etc.), food source (e.g., specific restaurants or restaurant chains, grocery stores, particular pre-packaged foods, school meals, meals prepared at home, etc.). Given the trained models, the Food Logger automatically provides estimates of nutritional information based on automated recognition of new images of meals provided by (or for) the user. This nutritional information is then used to enable a wide range of user-centric interactions relating to food consumed by individual users.
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Citations
20 Claims
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1. A computer-implemented process for evaluating images of meals, comprising:
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using a computer to perform process actions for; receiving a meal image of a single meal consisting of multiple food items; wherein one or more food items are visible and one or more of the food items are occluded in the meal image; receiving information indicating a source of the meal; recognizing one or more of the visible food items and one or more of the occluded food items in the meal by evaluating the meal image using a machine-learned meal model that is constrained by the source of the meal; and presenting one or more interactive messages automatically generated based on the recognized food items. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system for recognizing meals, comprising:
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a general purpose computing device; and a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to; provide one or more machine-learned meal models trained on combinations of image features extracted from one or more sets of training images of representative meals and nutritional information corresponding to the representative meals; acquire a single meal image in which one or more food items are visible and one or more other food items are occluded; extract a plurality of image features from the meal image; recognize the visible food items and one or more of the occluded food items by applying one or more of the machine-learned meal models to the image features extracted from the meal image; generate one or more interactive messages in response to one or more of the recognized food items; and present one or more of the interactive messages via a user interface. - View Dependent Claims (11, 12, 13, 14)
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15. A computer storage device having computer executable instructions stored therein for recognizing meals from images of those meals, said instructions causing a computing device to execute a method comprising:
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receive a machine-learned meal model trained on combinations of one or more sets of training images of representative meals and sources associated with one or more of the representative meals; acquire a single current meal image of a meal comprising a plurality of food items; wherein one or more of the food items are visible in the current meal image and one or more other food items are fully occluded in the current meal image; receive information representing a source of a meal corresponding to the current meal image; recognize the visible food items and at least one of the occluded food items from the current meal image by evaluating the current meal image using the machine-learned meal model as constrained based on the source of the meal; and present one or more interactive messages automatically generated based on the recognized visible food items and the recognized occluded food items. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification