Multi-modal image segmenting method based on functional mapping

Abstract

The invention relates to a multi-modal image segmenting method based on functional mapping. For an image set comprising a target, the multi-modal image segmenting method comprises the following steps: (1), segmenting an image into superpixel blocks, and representing the superpixel blocks by using different feature descriptors to obtain a multi-modal image representation; (2), establishing a superpixel map on the multi-modal image, and constructing a corresponding Laplacian matrix; (3), representing a reduction functional space of each image, and establishing functional mapping between image pairs; (4), aligning the image functional mapping of each modal to an image thread, and introducing an implicit function to keep consistency between functional mapping; (5), obtaining a functional mapping representation according to the consistency of the multi-modal mapping, and calculating a segmentation function corresponding to an image through combining and optimizing an objective function to obtain an optimal segmentation representation of the image. According to the multi-modal image segmenting method, each target region block of the image can be accurately judged by using a target potential relevance shared between feature representations of different modals of the image and the image, so that the image segmentation performance and effect are enhanced.

Description

technical field [0001] The invention belongs to the technical field of image segmentation in image processing, in particular to a multimodal image segmentation method based on functional mapping. Background technique [0002] The vigorous development of digital image technology has spawned a large number of emerging industries, such as remote sensing satellite image positioning, medical image analysis, traffic intelligent identification, etc., which has promoted the maturity of the information society. As an important bridge for humans to perceive the world, images are also closely related to the visual field. For example, image processing is in increasing demand and playing an increasingly critical role in various visual applications in the fields of artificial intelligence, machine vision, physiology, medicine, meteorology, military science, etc. As an image preprocessing method, image segmentation has laid a solid foundation for high-level semantic analysis in images. Fo...

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Problems solved by technology

[0004] The shortcomings of the above methods are mainly manifested in the following aspects: first, direct processing of the original pixels of the image increases the time complexity of the algorithm and increases the computational overhead; second, the underlying processing techniques such as threshold segmentation and edge segmentation are very difficult It is difficult to establish an association with the semantic features of the image; third, the complementary information between images is ignored, especially there are some common structures and potential information between images containing similar objects, which directly affects the segmentation effect of image objects

Therefore, these methods are not suitable for large-scale image segmentation tasks that contain common targets, and thus have a certain adverse effect on practical applications such as image recognition and target positioning with large orders of magnitude.

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