Object segmentation method based on multiple-instance learning and graph cuts optimization

Abstract

The present invention discloses an object segmentation method based on multiple-instance learning and graph cuts optimization. The method comprises the first step of carrying out salient model construction by adopting a multiple-instance learning method on training images, and predicting packages and instances of a testing image by using a salient model, thus to obtain a saliency testing result of the testing image; a second step of introducing the saliency testing result of the testing image into a graph-cut frame, optimizing the graph-cut frame according to instance characteristic vectors and marks of the instance packages, acquiring a second-best solution of graph cuts optimization, and obtaining precise segmentation of an object. According to the method provided by the present invention, the saliency testing model is constructed by using the multiple-instance learning method and thus is suitable for images of specific types, the saliency testing result is used into an image segmentation method based on the graph theory so as to guide image segmentation, a graph cut model frame link is optimized, an agglomerative hierarchical clustering algorithm is adopted for solving, the segmentation result can thus well accords to semantic aware output, and an accurate object segmentation result can be obtained.

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 ...

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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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