Video image significance detection method based on dynamic color association

Abstract

The invention discloses a video image significance detection method based on dynamic color association, which comprises the following steps of: obtaining a static significance chart of the video image; extracting the optical flow vector field of a scene; performing preliminary classification to the optical flow vector field and putting the maximum classification block away; converting the video image to HSV (hue, saturation, value) color space from RGB (red, green, blue) color space; generating a color histogram according to the frequency of the corresponding color in the H vector of the HSV color space appearing in the input image; aiming at each vector in the effective classification block of the optical flow vector field, projecting the norm into corresponding zones of the color histogram to obtain the movement scale variable of each color zone; obtaining the dynamic significance value of each color and projecting to the initial image to generate a dynamic significance chart; and summing the dynamic significance chart and the static significance chart, thereby obtaining the final significance chart. The method disclosed by the invention can effectively bring the dynamic characteristic into the significance consideration range, and can obtain the result on the basis of the existing dynamic video test set, which is more excellent than the result of the traditional method.

Description

technical field [0001] The invention belongs to the technical field of video image processing, and in particular relates to a video image saliency detection method. Background technique [0002] Recognizing important objects from complex scenes is a fundamental function of the human visual nervous system. For example, traffic lights can attract the attention of the human eye while driving, an airplane flying in the blue sky can attract the attention of the human eye, and a lighthouse at sea level can attract the attention of the human eye at night. Relying on this function, we can focus on the key parts to achieve better analysis results. [0003] Saliency detection is to enable the computer system to imitate the attention mechanism of the human eye, and highlight the important parts of the video image through the corresponding calculation process, which is a "discovery" process. Using the results of saliency detection, various scarce resources can be allocated preferentia...

AI Technical Summary

Problems solved by technology

[0007] Wixson et al. proposed a directional constant flow detection method in 2000, but it assumes that the target moves along a straight line, which is difficult to adapt to most application scenarios

Mahadevan et al. proposed a center-surround spatiotemporal saliency detection method in 2010. Its results strongly depend on the size of the detection window, and it is prone to detection failures for larger foreground objects.

Gopalakrishnan proposed a motion saliency detection method for linear dynamic contours in 2012. This method can only perceive the general position of the target, cannot generate a complete contour, and has poor accuracy.

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