Method and system for image segmentation

Abstract

The embodiment of the invention provides a method and a system for image segmentation. The method comprises the following steps: pre-segmenting a given image to obtain a plurality of sub-areas; merging the plurality of sub-areas according to at least two features to obtain sub-segmentation result corresponding to each feature; indicating sub-segmentation results corresponding to at least two features as hypergraphs; and performing cluster ensemble on the sub-segmentation results of the at the least two features based on the hypergraphs to obtain the segmentation result for the given image. The method is effectively combined with segmentation for the image based on multiple features, overcomes the defect that a common segmentation algorithm has no generality based on single image feature and avoids the problem that the features have no due effect on segmentation performance due to improper setting of visual feature vector and partial weights and dimensions in Bag of words.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image segmentation method and system. Background technique [0002] Image segmentation is the first step in image processing and analysis, the core technology of image target recognition, and one of the oldest and most difficult problems in image processing. Image segmentation is to represent an image as a collection of physically meaningful connected regions, that is, to mark and locate the target and background in the image according to the prior knowledge of the target and the background, and then separate the target from the background or other pseudo targets. process of separation. [0003] There are many methods for image segmentation. Any segmentation algorithm based on a single feature can only produce better segmentation results on image classes that are sensitive to this feature, while the performance of segmentation for images that are not suitable...

AI Technical Summary

Problems solved by technology

[0004] In the prior art, the multi-feature segmentation method is combined by combining multiple extracted global features into a high-dimensional feature vector. Since each visual feature occupies a different dimension, the high-dimensional features are often in the segmentation process. It plays a leading role, but other features are difficult to play a role, and the performance of segmentation is difficult to improve; the method based on bag of words (Bag of words) is used to combine multiple extracted global features and local features into visual words to fuse multiple features. Due to the different properties of global features and local features, the formed visual words are also obviously different. Therefore, simply combining these two visual words directly in segmentation cannot play the role of each feature, and the performance of segmentation is difficult to improve.

Due to the improper setting of the dimensions and weights of each part in the visual feature vector and Bag of words, the features cannot play their due role in the performance of the segmentation. Therefore, these methods cannot play the role of each feature in the segmentation.

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