Visual Salient Object Recognition Algorithm Based on Parzen Window Algorithm
It is difficult to automatically acquire image semantic information due to the increasing complexity of the image,and the traditional target saliency detection method for acquiring the image information has the problems of low stability and poor accuracy,so the invention provides an optimization algorithm based on improved Parzen window target position estimation,and a PAR-SVM model for classification and recognition of salient objects in images is con-structed.Firstly,the image was processed by binary value processing and morphology,and the density position estima-tion of the salient target was carried out by using the improved Parzen window algorithm,then the G and H features of the salient target position in the image were extracted,and the data set was planned after organic fusion,and finally the salient target recognition model of PAR-SVM image was constructed based on the data-driven method,and the model parameters were optimized by cross-validation.The simulation results of the first ablation experiment show that the accuracy of the model is improved by 19.12%compared with that before optimization.The simulation results of the second experiment show that the accuracy of the PAR-SVM algorithm is as high as 86.5%on the SOD data set,with an average increase of 3.14%,and the stability is as high as 86.0%on the SOD data set,with an average in-crease of 2.3%,compared with other five classification and recognition algorithms.To sum up,the image salient object recognition model based on the improved Parzen window algorithm in this paper improves the detection accuracy as well as the stability of the model.
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