Adaptive control deep learning and knowledge mining image classification
WANG Chun-hua
HAN Dong
Abstract：Aiming at the boundedness of traditional classification methods, an adaptive image classification algorithm for zero sample images in combination with both depth learning and knowledge mining was proposed. With the deep learning of image attributes, the learning and forecast of deep-level features and attributes of images were realized. Based on the attribute-class mapping of the images,the classifier had the great performance differences. The relationship between the image categories and attributes was characterized by the sparse representation, and an attribute classifier with adaptive control was designed to realize the classification operation of images. The results show that compared with both DBN and SVM algorithms,the proposed algorithm has high attribute prediction accuracy under both supervised mode and zero sample mode. When the Shoes data set was classified under the condition of zero sample, the proposed algorithm has the highest accurate classification recognition rate,which is 15% higher than other algorithms.
Keywords：deep learningknowledge miningconvolution neural networkimage classificationzero samplesupport vector machinesdeep belief networkclassifier
Publication Date：2018-05-02
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 334-339 )