Characteristic self-adaption image common segmentation method based on image complexity

Abstract

The invention provides a characteristic self-adaption image common segmentation method based on image complexity. The method includes in consideration of accurate detection results of an object detection method in a simple background image and dispersing detection results in a complex image and in consideration of initial segmentation accuracy, firstly starting from segmentation results of the simple background image, then self-adaption adjusting a common segmentation parameter by learning corresponding optimal similarity level measurement criteria of different image groups, and using the common segmentation parameter which is obtained by self-adaption adjusting to carry out image common segmentation handling. The method is high in detection rate, accurate in result and strong in self-adaption ability.

Description

technical field [0001] The invention relates to image processing technology, in particular to adaptive image co-segmentation technology. Background technique [0002] With the development of the network, there are a large number of digital images in the network. Using these massive images to achieve specific object detection and segmentation has become more and more concerned. [0003] Co-segmentation is the technique of segmenting a specific object from multiple images containing the same specific object with different backgrounds. In order to segment specific objects in the current image, the existing co-segmentation methods first introduce multiple images containing the same specific object, such as images obtained by search engines, and then achieve this by extracting and segmenting the common objects contained in this group of images. Segmentation of specific objects. [0004] The advantage of the co-segmentation method is that only the user is required to introduce ...

AI Technical Summary

Problems solved by technology

In fact, the same features corresponding to common objects in different image groups are very different. If the co-segmentation parameters are not adjusted in time, the accuracy of the co-segmentation results will be affected.

However, as an automatic image co-segmentation method, people often cannot participate in the selection of feature parameters, and it is difficult to manually adjust the feature parameters involved in segmentation.

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