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Image automatic marking method based on Monte Carlo data balance

An image automatic labeling and image technology, applied in the field of computer vision and image processing, can solve the problems of high cost, not considering the corresponding relationship between image areas and keywords, and enlargement

Active Publication Date: 2016-06-22
FUZHOU UNIV
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Problems solved by technology

Because the SVM problem is usually a convex quadratic programming problem, a large cost is required in the solution process, and with the geometric growth of the classification number, this cost will continue to increase, and this method does not take into account the image area and The corresponding relationship of keywords leads to unsatisfactory labeling effect

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  • Image automatic marking method based on Monte Carlo data balance
  • Image automatic marking method based on Monte Carlo data balance
  • Image automatic marking method based on Monte Carlo data balance

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Embodiment Construction

[0075] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0076] This embodiment provides an image automatic labeling method based on Monte Carlo data equalization, such as figure 1 shown, including the following steps:

[0077] Step S1: Automatically segment the training set images in the public image database;

[0078] Step S2: Use the comprehensive distance image feature matching method (CDIFM) to automatically match the segmented images, classify images with the same features and similar features into one category, and paste corresponding tag words; different categories of image sets have Tags for different descriptions;

[0079] Step S3: performing Monte Carlo data set equalization (MC-BDS) on image sets of various categories with different tag words, images of each category have the same tag word, and image sets of different categories have different descriptors;

[0080] Step S4: Extract the multi-sc...

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Abstract

The present invention relates to an image automatic marking method based on Monte Carlo data balance. The method comprises the steps of carrying out the region segmentation on the training sample images in a public image library, enabling the segmented regions possessing different characteristic description to correspond to one marking word, then carrying out the Monte Carlo data balance on the different types of image sets, extracting the multiscale characteristics of the balanced images, and finally inputting the extracted characteristic vectors in a robustness least squares increment limit learning machine to carry out the classification training to obtain a classification model in the image automatic marking; for the to-be-marked images, carrying out the region segmentation on the to-be-marked images, adopting the same multiscale characteristic fusion extraction method and inputting the extracted characteristic vectors in the least squares increment limit learning machine to obtain a final image marking result. Compared with a conventional image automatic marking method, the method of the present invention enables the images to be marked more effectively, is strong in timeliness, can be used for the automatic marking of the large-scale images, and possesses the actual application meaning.

Description

technical field [0001] The invention relates to the fields of computer vision and image processing, in particular to an image automatic labeling method based on Monte Carlo data equalization. Background technique [0002] Image understanding is the semantic understanding of images. It regards images as objects and knowledge as its core, and focuses on the research on the objects in images, the relationship between objects, and the scenes depicted in images based on people's cognition. The ultimate goal of image semantic understanding is to meet people's different needs for images. Fully understanding the hidden semantic content in images is an important step in image management. Earlier, the construction of image semantic databases was often done manually. However, with the explosive growth of the number of images, if the semantics of images is still marked manually, it will consume huge manpower and material resources, and it is not feasible. In addition, due to certain d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/24G06F18/214
Inventor 柯逍杜明智周铭柯
Owner FUZHOU UNIV
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