A Method and System for Extracting Regions of Interest in Images Based on Two-step Clustering of Eye Track Data

Abstract

The present invention provides a method for extracting image regions of interest based on two-step clustering of eye movement data, including a pre-clustering stage and a clustering stage; wherein the pre-clustering stage aims to eliminate noise points in the eye movement data, and input test pictures Based on the eye movement trajectory point data of multiple people on the Internet, the trajectory point data of each two people are combined for clustering, and the noise points are eliminated based on the recognition standards at the point level and the class level, and the eye movement trajectory points belonging to the effective fixation process are retained. The clustering stage aims to extract the region of interest from the effective eye movement trajectory points, and takes all the eye movement trajectory points belonging to the effective fixation process as input, and first clusters the points, where each class represents a region of interest, and the center represents The location of the region of interest, and the number of points in the class represents how much the region attracts attention. The present invention analyzes the trajectory points of multiple people through two-step clustering, and the extracted result of the region of interest is more in line with the human visual attention mode, and has better stability and anti-noise interference ability.

Description

technical field [0001] The invention relates to the field of eye movement data analysis, in particular to a method and system for extracting regions of interest based on two-step clustering of eye movement data. Background technique [0002] The eye tracker can be used to record the eye movement characteristics of the human eye when processing visual information, including eyeball position, fixation duration and fixation position and other related information. With the intelligent development of eye tracker hardware and related software, eye movement data is widely used in psychological Academic research fields such as science and medicine, and commercial fields such as advertising and web page optimization. Among them, one of the application hotspots of the eye tracker is to find the user's interest area for a certain visual target through the distribution characteristics of the user's eye movement data. Correspondingly, extracting image regions of interest based on eye mo...

AI Technical Summary

Problems solved by technology

However, when the target contains content of different shapes and randomly distributed, such as natural scene images, the manual labeling method cannot efficiently complete the task of extracting the region of interest, especially when the number of images is large, the manual labeling method needs to consume a lot of time. time and manpower

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