Image retrieval method based on spectrum analysis

Abstract

The invention provides an image retrieval method based on spectrum analysis. The method comprises the following five steps: performing layering super-pixel segmentation based on linear iteration to extract and remove the redundancy of an image and lower the complexity of image processing; performing dual-adjustment and distance definition on a two-dimensional image, and counting the dual-adjustment and distance distribution of the image to describe the local structural characteristics of the image; performing image sub-block segmentation based on spectral clustering to fuse more image internal implication structural characteristic information, wherein the image sub-block segmentation is more visually significant than the single super-pixel; performing image descriptor sub-design based on a word bag model, and performing statistical analysis on the frequency of the appearance of image characteristics to describe the components and the entire structure of the image. Via retrieval of an image descriptor based on spectrum analysis, the effectiveness of the method is proven through a practical image retrieval result. The image retrieval method designed by the invention has the characteristics of high distinguishing capability and high flexibility.

Description

technical field [0001] The present invention designs an image retrieval method that fuses object information and image global information image descriptors by combining the local feature measurement with the global spectrum analysis of the image, and makes the descriptors have scale invariance and non-rigid deformation invariance , to meet the needs of image retrieval and matching. Background technique [0002] 83% of the information obtained by human beings comes from vision, 11% from hearing, and others from smell, touch and taste, and people accept the rich information conveyed by images most fully and retain the longest memory time. Although images are all-encompassing and diverse, people can generally instinctively analyze the content structure of images and make use of them. In the fields of biomedicine, security, criminal investigation, graphic design, and media entertainment, people have subtly used The content correlation between image sets is used to assist image ...

AI Technical Summary

Problems solved by technology

[0005] However, from the perspective of image descriptor design, although image local feature descriptors represented by SIFT (Scale Invariant Feature Transform), SURF (Speeded Up Robust Features), and HOG (Histograms of Oriented Gradients) have been able to obtain more accurate However, most of these descriptors are designed for the pixel features of the image. The descriptor only encodes the local information distribution of the image, so it is difficult to directly describe the lesion or even the entire image. large scale object

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