Simulation of Image Large Data Classification Algorithm Based on Visual Descriptor
Image big data is an irresistible scientific and technological process,but with the increase of the number of images,traditional classification algorithms have certain limitations in image recognition and classification.In order to solve the problem of low accuracy of large data image classification,this paper proposes a classification al-gorithm that integrates image visual descriptors and image primary features.Firstly,the primary features of the image were extracted from the maximum pooling layer of VGG18 by using the advantages of transfer learning,and then an image preprocessing was added,and the homogeneous texture descriptor and the edge histogram descriptor were ex-tracted by using the"82 circular LBP operator"and the"Canny operator"respectively.Finally,an image recognition and classification model based on support vector machine(DES-SVM)was constructed by fusing basic image features and visual descriptors.Simulation results show that the proposed method can effectively improve the accuracy of image classification.Compared with the traditional SVM model,the accuracy,recall and F index of the DES-SVM model on the UKB image database and ZBD image database are increased by 7.85%,8.42%and 8.13%respectively.The DES-SVM image recognition and classification model constructed in this paper effectively improves the performance of the model through the extraction of visual descriptors.
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