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Small sample food image recognition model training method and food image recognition method

An image recognition and model training technology, applied in the field of food recognition, can solve the problem of inability to fine-grained division of images, and achieve the effect of improving classification performance

Pending Publication Date: 2020-04-24
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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Problems solved by technology

[0005] In the current small-sample recognition methods, it is impossible to divide the image into fine-grained

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  • Small sample food image recognition model training method and food image recognition method
  • Small sample food image recognition model training method and food image recognition method
  • Small sample food image recognition model training method and food image recognition method

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Embodiment Construction

[0036] In order to make the objectives, technical solutions, design methods, and advantages of the present invention clearer, the following further describes the present invention in detail through specific embodiments with reference to the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

[0037] Introduction to small sample learning

[0038] In small sample learning problems, training and testing samples are usually composed of a series of training sets and test sets. Suppose there are C training categories, and there are a total of N labeled training samples, define the training set among them Refers to the sampled image, Refers to s Mark. For the test set, suppose there are L new categories and there are a total of M test samples, define the test sample set Its label set is It is worth noting that the sample spaces of the training set and the...

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Abstract

The invention provides a small sample food image recognition model training method and a food image recognition method. The model training method comprises the following steps: constructing a triple comprising positive and negative samples and an anchor image by using a training data set, and inputting the triple into a ternary convolutional neural network to extract feature representation of thetriple; carrying out feature map fusion to obtain a positive and negative sample image pair feature map; and screening the positive and negative sample images based on the relationship value, and training the feature embedding network and the relationship learning network by using the screened positive and negative sample images.

Description

Technical field [0001] The invention relates to the technical field of food recognition, in particular to a small-sample food image recognition model training method and a food image recognition method combining a ternary convolutional neural network and a relational network. Background technique [0002] Food identification is an important research topic in the fields of computer vision, data mining, and multimedia social interaction. It has a wide range of applications in food automation inspection, food management, food trend and popularity analysis, smart home and food safety. Food data sets collected from the real world conform to the typical long-tailed distribution, that is, many uncommon food categories can only collect a small number of samples, so the identification of small samples of food is an urgent problem to be solved. [0003] However, there is currently no related work focusing on image recognition of small samples of food. This is because small-sample food image...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/214G06F18/241G06F18/253
Inventor 闵巍庆吕永强蒋树强
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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