Method for detecting degree of visual saliency of image in different regions

Abstract

The invention discloses a method for detecting the degree of visual saliency of an image in different regions, which comprises the following steps: segmenting the input image into non-overlapping image blocks, and carrying out vectorization on each image block; carrying out dimensionality reduction on all vectors obtained in the step 1 through the PCA principle component analytical method for reducing noise and redundant information in the image; calculating the non-similarity degree between each image block and all the other image blocks by utilizing the vectors after the dimensionality reduction, calculating the degree of visual saliency of each image block by further combining with the distance between the image blocks and obtaining a saliency map; imposing central bias on the saliency map, and obtaining the saliency map after imposing the central bias; and smoothing the saliency map after imposing the central bias through a two-dimensional Gaussian smoothing operator, and obtaining a final result image which reflects the degree of saliency of the image in all the regions. Compared with the prior art, the method does not need to extract visual features, such as color, orientation, texture and the like and can avoid the step of selecting the features. The method has the advantages of simpleness and high efficiency.

Description

technical field [0001] The invention relates to local area analysis in image processing, in particular to a visually salient area detection method in an image. Background technique [0002] The computing power of modern high-speed computers has reached staggering levels, but computer vision systems are unable to guide visual tasks such as crossing the road, which are very simple for humans. This is mainly because in the face of massive visual information input, the human eye can selectively focus on significant changes in the visual scene in a short period of time, and analyze and judge, so as to adapt to changes in the environment. A computer vision system, on the other hand, treats all regions of the visual scene equally indiscriminately, causing computational bottlenecks while being unable to understand scene changes. If we introduce the selective attention function of the human visual system into the computer vision system, it is bound to improve the efficiency of the e...

AI Technical Summary

Problems solved by technology

The computer vision system, on the other hand, treats all areas of the visual scene equally indiscriminately, and can cause computational bottlenecks while being unable to understand scene changes.

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