Image classification has received extensive attention as an important technical means of acquiring image information. It has been widely used in various engineering fields. 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 is because this type of method relies on the designer’s prior knowledge and cognitive understanding of the classification task. At the same time, this method separates image feature extraction and classification into two steps for classification operation. However, 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. At the same time, the image classification method based on deep learning also 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. To this end, this paper introduces the idea of sparse representation into the architecture of deep learning network, comprehensively utilizes the sparse representation of good multidimensional data linear decomposition ability and the deep structural advantages of multi-layer nonlinear mapping to complete the complex function approximation in deep learning model. It constructs a deep learning model with adaptive approximation ability, which solves the function approximation problem of deep learning models. At the same time, in order to further improve the classification effect of the deep learning classifier, 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. Based on the above explanation, 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 well adapted to various image databases. This is because the proposed method can extract more image feature information than the traditional image classification method and can better adaptively match the image information. 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.

作为获取图像信息的重要技术手段，图像分类受到了广泛的关注。它已广泛应用于各个工程领域。尽管现有的传统图像分类方法已在实际问题中得到了广泛应用，但在应用过程中仍存在一些问题，如效果不理想，分类精度低，自适应能力差等。这是因为这种类型的方法依赖于设计人员对分类任务的先验知识和认知理解。同时，该方法将图像特征提取和分类分为两个步骤进行分类操作。但是，深度学习模型具有强大的学习能力，将特征提取与分类过程集成在一起，完成图像分类测试，可以有效提高图像分类精度。同时，基于深度学习的图像分类方法在应用过程中还存在以下问题：第一，在深度学习模型中无法有效地逼近复杂函数。其次，深度学习模型附带一个分类器，其准确性较低。为此，本文将稀疏表示的思想引入了深度学习网络的架构，全面利用了良好的多维数据线性分解能力的稀疏表示和多层非线性映射的深层结构优势，完成了深度学习模型中的复杂函数逼近。构建具有自适应逼近能力的深度学习模型，解决了深度学习模型的函数逼近问题。同时，为了进一步提高深度学习分类器的分类效果，提出了一种基于优化核函数的稀疏表示分类方法来代替深度学习模型中的分类器，从而提高了图像分类效果。根据以上说明，提出了一种基于堆叠稀疏编码深度学习模型优化的核函数非负稀疏表示的图像分类算法。实验结果表明，该方法不仅具有比其他主流方法更高的平均精度，而且可以很好地适应各种图像数据库。这是因为与传统的图像分类方法相比，该方法可以提取更多的图像特征信息，并且可以更好地自适应地匹配图像信息。与其他深度学习方法相比，可以更好地解决函数逼近，分类效果差的问题，从而进一步提高了图像分类的准确性。实验结果表明，该方法不仅具有比其他主流方法更高的平均精度，而且可以很好地适应各种图像数据库。这是因为与传统的图像分类方法相比，该方法可以提取更多的图像特征信息，并且可以更好地自适应地匹配图像信息。与其他深度学习方法相比，可以更好地解决函数逼近，分类效果差的问题，从而进一步提高了图像分类的准确性。实验结果表明，该方法不仅具有比其他主流方法更高的平均精度，而且可以很好地适应各种图像数据库。这是因为与传统的图像分类方法相比，该方法可以提取更多的图像特征信息，并且可以更好地自适应地匹配图像信息。与其他深度学习方法相比，可以更好地解决函数逼近，分类效果差的问题，从而进一步提高了图像分类的准确性。