Image Classification Algorithm Based on Deep Learning-Kernel Function

• Published in: Scientific programming 2020, Vol.2020 (2020), p.1-14
• Main Authors: Liu, Jun-e, An, Feng-Ping
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation; Artificial intelligence; Classification; Classifiers; Datasets; Deep learning; Dictionaries; Feature extraction; Image classification; Kernel functions; Machine learning; Mapping; Mathematical analysis; Methods; Multidimensional data; Multilayers; Neural networks; Representations; Sparsity
• ISSN: 1058-9244; 1875-919X
• DOI: 10.1155/2020/7607612

Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy, and weak adaptive ability. This method separates image feature extraction and classification into two steps for classification operation. The deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. However, this method has the following problems in the application process: first, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. And a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy.