Semantic segmentation of weakly supervised images based on iterative mining of common features of objects

A technology of semantic segmentation and common features, applied in the field of pattern recognition, can solve the problems of increased difficulty in neural network design and training, achieve reliable semantic segmentation, reduce application costs, and have a wide range of application prospects

Active Publication Date: 2018-12-21
TSINGHUA UNIV
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Problems solved by technology

[0006] However, under the condition that only image category labels are used as supervision information, the difficulty of

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  • Semantic segmentation of weakly supervised images based on iterative mining of common features of objects
  • Semantic segmentation of weakly supervised images based on iterative mining of common features of objects
  • Semantic segmentation of weakly supervised images based on iterative mining of common features of objects

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Embodiment Construction

[0051] The weakly supervised image semantic segmentation method based on iteratively mining the common features of objects proposed by the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The following examples are only used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0052] The weakly supervised image semantic segmentation method based on the iterative mining object common feature proposed by the present invention is divided into a training stage and a use stage, including the following steps:

[0053] 1) Training phase; the overall process is as follows figure 1 As shown, the specific steps are as follows:

[0054] 1-1) Obtain a training data set;

[0055] The training data set includes training images and category labels corresponding to the images. When constructing the training data set, it is first necessary to define the obje...

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Abstract

The invention provides a weak supervised image semantic segmentation method based on iterative mining common features of objects, belonging to the technical field of pattern recognition. In the training phase, the method acquires the training data set, constructs and trains the multi-label classification network, and acquires the initial seed area corresponding to each training image. Then, the superpixel region and the region label of each training image are obtained for training the region classification network, and the updated region label of the superpixel region is obtained for trainingthe semantic segmentation network. After iteration, when the performance of the semantic segmentation network converges, the trained semantic segmentation network is obtained. In the use stage, the color image is input into the trained semantic segmentation network, and the network outputs the semantic segmentation results of the image. The invention can realize reliable pixel-level semantic segmentation under the condition of only image class label, reduces the time and labor cost of data labeling, and has wide application prospect.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a weakly supervised image semantic segmentation method based on iterative mining of common features of objects. Background technique [0002] Image semantic segmentation is an important research direction in pattern recognition and computer vision. It refers to the full understanding of image content through pixel-level recognition and segmentation of images. Therefore, it has a very broad application prospect in the fields of automatic driving and robot vision. . [0003] In intelligent application scenarios such as autonomous driving, the on-board computer needs to complete the perception of the scene first, and make corresponding movements and decisions based on the perceived information. Image semantic segmentation is an important part of visual scene perception. Image semantic segmentation realizes the computer's understanding of the pixel level of t...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/241G06F18/214
Inventor 马惠敏汪翔李熹
Owner TSINGHUA UNIV
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