Fixation Detection Based On Semantic Features

When people see the any size of the complex of the images,they have a significantly rapid region identified from the image ability,because of this ability,human can select the information that they need from images.So for this kind of simulating human vision mechanism also can lets the robot have the ability to make the computer can also have the same human like screening information,so that visual fixation detection has become a hot research field.The so-called fixation detection is to let the computer or visual attention system to simulate human,which can make computers or robots select fixation in a picture of areas of concern,and finally get a fixation saliency map,saliency map pixel value to indicate whether the size of the human fixation belongs to the probability of attention the order for further image processing,such as object recognition,artificial intelligence etc..Based on this,this paper puts forward three kinds of gradual fixation detection models,respectively from the traditional method and the deep of the neural network perspective to explore how to use the characteristics of each levels in the image to guide the visual fixation attention model for image detection.First of all,the first in the conventional method,the low level features in the image of the fixation of image detection,which roughly predict the human image in the areas of concern,then I use the image semantic segmentation for image semantic feature extraction.Finally,the support vector machine model for image fusion and feature of the proposed training,thus we can obtain the image of traditional models predict human fixation;second and third models are based on the deep of the neural network model on the establishment of the two models using the convolution layer depth the neural network can be automatically extracted from the image from low to high the features of each level in the image characteristics to the fixation detection model.In addition,based on the convolutional network focus detection model after network training is completed,the output will also get threshold processing,remove the noise in the image,and then the initial detection output of the network graph and method in the fixation to get the final model by support vector machine.Finally,the third model proposed in this paper is a model of human fixation based on the idea of recurrent convolutional network.In the process of establishing the model through the analysis of each layer in the whole network convolution convolution presents features of different levels and these features in human attention model plays the roleof the different network structure of convolutional network was improved,so as to get a new full recurrent convolution network.In the experimental part,the human fixation in a standard is now recognized as the three models of the proposed data sets were tested,and two aspects respectively from the traditional method and the deep of the neural network method with the existing various human fixation detection algorithms are compared.Through the comparison of the experimental results,it is proved that the three algorithms presented in this paper have better performance.This algorithm has reached the top level of human fixation detection.