Automatic picture labeling method and device

A picture and label technology, applied in the field of picture automatic labeling method and device, can solve the problems of poor accuracy, low labeling efficiency, cumbersome and complicated process, etc.

Pending Publication Date: 2020-05-01
北京视觉大象科技有限公司
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Keyword tags in image metadata play a key role in the review, retrieval and organization of massive images. However, the process of manually producing tags is cumbersome and complicated. Therefore, automatic tag generation has always been a key research field in computer vision and artificial intelligence. Image The automatic generation of tags has a wide range of application scenarios, which can effectively improve the efficiency of imag...

Method used

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  • Automatic picture labeling method and device

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Experimental program
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Embodiment 1

[0050] see figure 1 , the present embodiment provides a method for automatic labeling of pictures, comprising: training a multimodal feature extraction model based on gallery data, where the gallery data includes a plurality of pictures and label groups and classifications corresponding to each picture; According to the corresponding relationship between classification and classification, the visual semantic similarity nearest neighbor index of the picture and the label group is constructed; the features of the picture to be checked are extracted through the feature extraction model to obtain the feature vector, and based on the feature vector and the visual semantic similarity nearest neighbor index is matched from the gallery data. Similar pictures; according to the frequency and weight of the keywords in the corresponding label group of similar pictures, the initial labels of the pictures to be checked are screened out; the pre-trained word vector model is used to perform la...

Embodiment 2

[0070] This embodiment provides an automatic picture labeling device, including:

[0071] The feature extraction model training unit is used to train a multi-modal feature extraction model based on the gallery data, and the gallery data includes multiple pictures and the label group and classification corresponding to each picture;

[0072] The nearest neighbor index construction unit is used to construct the visual semantic similar nearest neighbor index of the picture and the label group according to the corresponding relationship between each picture and the label group and the classification;

[0073] The screening unit is used to extract the features of the picture to be checked through the feature extraction model to obtain a feature vector, and match similar pictures from the gallery data based on the feature vector and the visual semantic similarity nearest neighbor index;

[0074] An initial tag recognition unit, configured to filter out the initial tag of the image t...

Embodiment 3

[0085] This embodiment provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is run by a processor, the steps of the above method for automatically labeling pictures are executed.

[0086] Compared with the prior art, the beneficial effect of the computer-readable storage medium provided by this embodiment is the same as that of the automatic image labeling method provided by the above technical solution, and will not be repeated here.

[0087] Those of ordinary skill in the art can understand that all or part of the steps in the above-mentioned inventive method can be completed by instructing related hardware through a program. The above-mentioned program can be stored in a computer-readable storage medium. When the program is executed, it includes: For each step of the method in the above embodiments, the storage medium may be: ROM / RAM, magnetic disk, optical disk, memory card, and the like...

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Abstract

The invention discloses an automatic picture labeling method and device, relates to the technical field of picture processing, and aims to realize simultaneous labeling of multiple labels of a pictureand improve the accuracy of the labels while ensuring the labeling efficiency. The method comprises the following steps: training a multi-modal feature extraction model based on gallery data; constructing a visual semantic similarity nearest neighbor index of each picture and the label group according to the corresponding relationship between each picture and the label group and the classification; extracting features of a to-be-detected picture through a feature extraction model to obtain a feature vector, and matching a similar picture from the picture library data based on the feature vector and the visual semantic similarity nearest neighbor index; screening out an initial label of the to-be-detected picture according to the frequency and the weight of the keyword in the label group corresponding to the similar picture; and performing label filtering and weight sorting on the initial labels by adopting a pre-trained word vector model to obtain a final label group of the to-be-detected picture. The device applies the method provided by the scheme.

Description

technical field [0001] The invention relates to the technical field of picture processing, in particular to a method and device for automatic picture labeling. Background technique [0002] In recent years, with the extreme popularity of camera terminals, the vigorous development of self-media and the great abundance of Internet resources, how to effectively review, retrieve, extract and organize copyrighted media content has become a major challenge for the gallery industry. Keyword tags in image metadata play a key role in the review, retrieval and organization of massive images. However, the process of manually producing tags is cumbersome and complicated. Therefore, automatic tag generation has always been a key research field in computer vision and artificial intelligence. Image The automatic generation of tags has a wide range of application scenarios, which can effectively improve the labor efficiency of image editing and review, and optimize the structure of the prod...

Claims

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Application Information

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IPC IPC(8): G06F16/55G06K9/62G06N3/04G06N3/08
CPCG06F16/55G06N3/08G06N3/045G06F18/24G06F18/214
Inventor 杨巍陈韬齐欣
Owner 北京视觉大象科技有限公司
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