Fixation point detection method based on local evaluation and global optimization

A technology of global optimization and detection method, which is applied in the field of gaze point detection to achieve the effect of improving the effect and improving the results of gaze point detection.

Inactive Publication Date: 2017-05-31
DALIAN UNIV OF TECH
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is: when the image contains one or more salient objects, when there are semantic information s

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  • Fixation point detection method based on local evaluation and global optimization
  • Fixation point detection method based on local evaluation and global optimization
  • Fixation point detection method based on local evaluation and global optimization

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[0034] The specific implementation manners of the present invention will be further described below in conjunction with the accompanying drawings and technical solutions.

[0035] Step 1: The candidate frames in the present invention are obtained by the edge box algorithm, which generates 10,000 candidate frames by default. We observed that keeping more candidate boxes can ensure that the candidate set contains all the gaze regions as much as possible, while keeping fewer candidate boxes can increase the efficiency of the algorithm. In order to maintain a balance between accuracy and efficiency, we set the edge box parameter α to 0.65, β to 0.55, and retain 2000 candidate boxes for each picture.

[0036] Step 2: As mentioned above, the convolutional neural network we use is a vgg-16 structure, and the initial parameters are also the parameters of the vgg-16 image classification network. In the process of fine-tuning network parameters, we use the salicon database as training ...

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Abstract

The invention belongs to the field of computer vision, and discloses a fixation point detection method based on local evaluation and global optimization. The method comprises the steps of extracting possible candidate targets in an image; then utilizing a supervised learning method to conduct local evaluation on the targets, wherein two evaluation methods are as follows, (1), grading significance of each proposals using image training SVM of a whole database; (2), utilizing a semi-coupled dictionary learning algorithm, reconstituting different SVM aiming at different images, and grading the proposals of the image with pertinence; after local evaluation, utilizing a proposal subset optimization algorithm to cluster proposals; finally conducting global optimization. According to fixation point detection method based on local evaluation and global optimization, aiming at the characteristics of different information catching the attention of human eyes, a model capable of seizing the information is designed, and the human eye gazing area in an image containing semantic information, an image containing objects, and an image which is complicated or does not contain the objects can be effectively detected.

Description

technical field [0001] The invention belongs to the field of computer vision, relates to relevant knowledge of image processing, and in particular relates to a gaze point detection method. Background technique [0002] As a branch of saliency detection, gaze point detection has broad application prospects in image segmentation, image compression, object recognition and other fields. In recent years, many novel algorithms have emerged in the field of eye movement point detection, and the detection effect on some databases has reached a good level, but there are still many key problems that have not been resolved. The classic algorithm of eye movement point detection is analyzed below, and the current development status is summarized. [0003] Itti et al. proposed the earliest fixation point prediction algorithm in the paper "A model of saliency-based visual attention for rapidscene analysis, 1998". They extract the color, brightness, and direction of the image, and use the ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/161G06V40/168G06F18/23G06F18/2411
Inventor 李建华姜博卢湖川
Owner DALIAN UNIV OF TECH
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