Image marking method combining local image constraint and overall target constraint

Abstract

The invention discloses an image marking method combining a local image constraint and an overall target constraint. The method comprises the following steps of acquiring a super pixel area node image of each original image, finding a mask area corresponding to each super pixel area node of the corresponding super pixel area node image in a mask image corresponding to each original image and marking; then, using a characteristic set of the super pixel area node image and an area label set corresponding to the mark image to train a conditional random field model, using a virtual label set corresponding to images acquired after all the mask areas are marked in the mask image to train a shape Boltzmann machine model; and through a grid partitioning technology, effectively combining the conditional random field model and the shape Boltzmann machine model. The local image constraint and the overall target constraint are tightly combined and accuracy of image marking is increased. The method can be suitable for a condition in which a data set is small and image resolution is low. And computation complexity is low.

Description

technical field [0001] The invention relates to an image marking technology, in particular to an image marking method combining image local constraints and object global constraints. Background technique [0002] Image segmentation and image labeling are the core technologies in computer vision. Nowadays, many high-level applications in computer vision rely on accurate image segmentation results or image labeling results, such as object recognition and scene analysis applications. Image segmentation and image labeling have always been one of the most challenging tasks in computer vision due to problems such as occlusion, shadows, and similarity between objects and background features in images. [0003] As the basis and challenging task of various vision applications, image marking technology has been widely concerned in the field of computer vision. How to better mark images has been deeply studied by relevant institutions at home and abroad. In recent years, since Conditi...

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Problems solved by technology

Chen F, Yu H, Hu R, et al.Deep Learning Shape Priors for ObjectSegmentation[C] / / Computer Vision and Pattern Recognition,2013 (Chen Fei, Yu Huimin, Hu Haoji, Zeng Xunxun, deep learning shape priors for images Segmentation [C] / / Computer Vision and Pattern Recognition Conference, 2013), which proposes to combine the object shape prior learned by the deep Boltzmann machine model into a variational segmentation model, and achieve segmentation by finding the minimum energy function The effect; this method combines the deep learning model into the variational segmentation framework, resulting in a large computational complexity

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