Object Segmentation Method Based on Multiple Instance Learning and Graph Cut Optimization

Abstract

The invention discloses a target segmentation method based on multi-instance learning and graph-cut optimization: Step 1: use the multi-instance learning method to carry out saliency model modeling on the training image, and use the saliency model to carry out saliency model modeling on the packages and examples in the test image Predict, get the saliency detection result of the test image; Step 2: Introduce the saliency detection result of the test image into the graph cut framework, optimize the graph cut framework according to the example feature vector and the label of the example package, and solve the suboptimal graph cut optimization solution to obtain an accurate segmentation of the target. The present invention adopts the method of multi-instance learning to establish a saliency detection model to make it suitable for specific types of images, and uses the results of saliency detection in an image segmentation method based on graph theory to guide image segmentation, and the frame link of the graph cut model It is optimized and solved by agglomerative hierarchical clustering algorithm, so that the segmentation results can better conform to the output of semantic awareness, and obtain accurate target segmentation results.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to an image segmentation method, in particular to an object segmentation method based on multi-instance learning and graph cut optimization. Background technique [0002] Image target segmentation is an important research direction in the field of computer vision, and it is also an important basis for applications such as visual inspection, tracking and recognition. The quality of its segmentation affects the performance of the entire visual system to a large extent. However, due to the lack of deep understanding of the human visual system, image segmentation has also become a classic problem in the field of computer vision. The human visual system is capable of selectively paying attention to the primary content of the observed scene while ignoring other secondary content. This selective attention mechanism of vision makes efficient information processing possible, and also inspires ...

AI Technical Summary

Problems solved by technology

At present, most saliency detection is based on unsupervised models. There are problems such as the lack of learning ability of the defined model, the calculation of saliency can not reflect the visual attention mechanism well, and the lack of adaptability and poor robustness to specific types of images. ; and the single use of the cost function-based graph cut algorithm for target segmentation also has problems such as high computational complexity, low segmentation efficiency and local segmentation accuracy

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