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An Image Semantic Annotation Method Based on Superpixel Segmentation

A technology of superpixel segmentation and semantic annotation, which is applied in the field of image semantic analysis based on superpixel segmentation and convolutional neural network, can solve problems such as the inability to universally solve image annotation problems, and achieve improved accuracy and robustness, The effect of improving computational efficiency and simplifying complexity

Active Publication Date: 2019-07-30
ZHEJIANG UNIV
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  • Application Information

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Problems solved by technology

These methods reflect completely different research ideas, but none of them can solve the problem of image annotation universally.

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  • An Image Semantic Annotation Method Based on Superpixel Segmentation
  • An Image Semantic Annotation Method Based on Superpixel Segmentation
  • An Image Semantic Annotation Method Based on Superpixel Segmentation

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Embodiment Construction

[0020] As shown in the figure, an image semantic annotation system based on superpixel segmentation, the semantic annotation system is divided into two parts: the first part is the superpixel block feature extraction part. The first part involves converting multi-level superpixel blocks into feature image blocks that can be input into the convolutional neural network for training, and for each superpixel block, it needs to be expanded with the geometric features of the superpixel, and requires A support vector machine is used to weight the features of the superpixel block. In the second part, the multi-level super-pixel features are integrated into the pixel level, and the pixel-level conditional random field model is established, and the reasoning is carried out through the idea of ​​​​great a posteriori margin, and the image can be obtained by solving the model Labeled results. The technical problem to be solved by the present invention is to provide an image semantic annot...

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Abstract

The present invention provides an image semantic labeling method based on superpixel segmentation. First, the feature blocks extracted based on image superpixel segmentation are input into the convolutional neural network, and then the feature vectors obtained by the convolutional neural network training are expanded and weighted. processing, and finally build a conditional random field model for semantic category annotation prediction. Due to the adoption of the technical solution of the present invention, the method takes superpixel blocks as the research object, which simplifies the complexity of feature blocks extracted based on image superpixel segmentation, and improves the computational efficiency of semantic annotation; in addition, multi-layer Semantic analysis is performed on superpixel blocks, and the annotation results are synthesized, which improves the accuracy and robustness of semantic annotation.

Description

technical field [0001] The present invention relates to the image semantic annotation method, in particular to the technical field of image semantic analysis based on superpixel segmentation and convolutional neural network. Background technique [0002] At present, the application of robots has expanded from traditional industrial manufacturing to military, scientific exploration and even medical services. In these new application areas, robots often work in unstructured outdoor environments. Compared with the indoor environment with single information, the outdoor scene is more complex, changeable and layered, involves a wide variety of semantic information, and is easily affected by factors such as light and field of view. In addition, there is no step-by-step operation steps when the robot works, and only a little prior knowledge, so the perception and understanding of the outdoor environment become a necessary prerequisite for autonomous control such as environmental m...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/214G06F18/24
Inventor 刘勇刘晓峰
Owner ZHEJIANG UNIV