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A method for automatic labeling of multi-view images

An automatic image labeling and multi-view technology, applied in the field of image processing, can solve the problems of ignoring the differences of different views, and achieve the effect of improving labeling performance and labeling performance

Inactive Publication Date: 2019-07-30
NORTH CHINA UNIVERSITY OF TECHNOLOGY
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  • Description
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  • Application Information

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Problems solved by technology

Multi-view sparse coding is an important branch of automatic image annotation, but in existing methods, each view often shares the same sparse coefficients, ignoring the differences of different views

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  • A method for automatic labeling of multi-view images
  • A method for automatic labeling of multi-view images
  • A method for automatic labeling of multi-view images

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Embodiment Construction

[0043] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below with reference to the accompanying drawings and preferred embodiments. However, it should be noted that many of the details listed in the specification are only for readers to have a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be implemented even without these specific details.

[0044] A method for automatic labeling of multi-view images proposed in this embodiment, its flow chart is as follows figure 1 As shown, it specifically includes the following steps:

[0045] (1) Set the semantic labels and various visual features of the marked images as multiple views, input them into the multi-view sparse model for training and learning, and obtain the view dictionaries and the weight factors of each view. Wherein, each view dictionary includes ...

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Abstract

The invention discloses a multi-view image automatic labeling method, which includes the following steps: (1) setting semantic labels and various visual features of the marked image as multiple views, inputting them into a multi-view sparse model for training and learning, and obtaining each view dictionary and each view weight factor; (2) input multiple visual features of the image to be marked; (3) use the view dictionary and each view weight factor to sparsely reconstruct the image to be marked, and calculate The sparse reconstruction coefficient of the label view; (4) multiply the sparse reconstruction coefficient of the label view dictionary and the label view to obtain the score of the semantic label of the image to be labeled; (5) divide the score from high to low Arrange, select the first 5 semantic tags to mark the image to be marked. The invention improves the automatic image labeling performance of the computer, and improves the precision rate and recall rate of the automatic labeling.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a multi-view image automatic labeling method. Background technique [0002] With the rapid development of multimedia information technology, the effective management and retrieval of massive image databases has become an urgent problem to be solved. Currently, text-based image retrieval methods are still an important method for many image search engines to retrieve relevant images. Therefore, the accuracy and efficiency of image retrieval will be greatly improved if keywords that reflect its semantic content are assigned to images in advance. Automatic image annotation is to let the computer complete this task automatically and intelligently. It uses labeled image sets or other available prior information to automatically learn the mapping relationship between semantic concept space and visual feature space, and uses this relationship to label images with unknown semantics. The...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/583G06F16/532G06K9/62
CPCG06F16/532G06F16/5838G06F18/2136
Inventor 臧淼
Owner NORTH CHINA UNIVERSITY OF TECHNOLOGY