Method for segmenting images by using increment dictionary learning and sparse representation

Abstract

The invention discloses a method for segmenting images by using increment dictionary learning and sparse representation, which mainly solves the problem that the accuracy rate of the sparse representation segmentation result is reduced quickly when the similarity among the categories is increased in the prior art. The method comprises the following steps of: obtaining clustering results of to-be-segmented image categories K by a K mean algorithm; training a part of results of each category serving as a training sample set to obtain K dictionaries; performing sparse representation on characteristic points of each unknown tag by using the dictionaries to obtain K sparse representation errors; performing classification by using the magnitude of the sparse representation errors, selecting a training sample set for increment learning based on the classification result, and retraining the training sample set to obtain K dictionaries; performing sparse representation on input signals by using the dictionaries to obtain K sparse representation errors; and performing final segmentation of the images by using the errors. Compared with the prior art, the method remarkably improves the segmentation performance of the images, and can be used for target detection and background separation.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an image segmentation method, which can be used to segment texture images. Background technique [0002] Image segmentation is one of the basic and key technologies in image processing and computer vision, and its purpose is to separate the target from the background. Image segmentation refers to the technology and process of dividing the image into regions with different characteristics and extracting the target of interest, which provides the basis for subsequent classification, identification and retrieval. Here, the feature can be the grayscale, color, texture, etc. of the pixel, and the pre-corresponding target can correspond to a single area or multiple areas. Image segmentation is widely used in almost all fields of image processing, such as medical image analysis, military research, remote sensing meteorology, traffic image analysis, etc. Image segmentation method...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that the correct rate is relatively dependent on the selection of the base. When a better sample is selected as the base, this method can achieve a better segmentation effect.

[0004] The K-means algorithm is faster. Although it can achieve better segmentation results, the disadvantage is that the selection of the initial point is random, so the segmentation results are not stable.

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