However, because the RCD method searches for the optimal solution in the entire real space, its solution may be negative. (5)Based on steps (1)–(4), an image classification algorithm based on stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation is established. The sparsity constraint provides the basis for the design of hidden layer nodes. When λ increases, the sparsity of the coefficient increases. It has 60,000 color images comprising of 10 different classes. The SSAE depth model directly models the hidden layer response of the network by adding sparse constraints to the deep network. Then, by comparing the difference between the input value and the output value, the validity of the SSAE feature learning is analyzed. Under the sparse representation framework, the pure target column vector y ∈ Rd can be obtained by a linear combination of the atom in the dictionary and the sparse coefficient vector C. The details are as follows: Among them, the sparse coefficient C = [0, …, 0, , 0, …, 0] ∈ Rn. Section 5 analyzes the image classification algorithm proposed in this paper and compares it with the mainstream image classification algorithm. Then, fine tune the network parameters. 1. , ci ≥ 0, ≥ 0. The SSAE deep learning network is composed of sparse autoencoders. Since the learning data sample of the SSAE model is not only the input data, but also used as the target comparison image of the output image, the SSAE weight parameter is adjusted by comparing the input and output, and finally the training of the entire network is completed. This is the main reason for choosing this type of database for this experiment. The deep learning algorithm proposed in this paper not only solves the problem of deep learning model construction, but also uses sparse representation to solve the optimization problem of classifier in deep learning algorithm. Jing, F. Wu, Z. Li, R. Hu, and D. Zhang, “Multi-label dictionary learning for image annotation,”, Z. Zhang, W. Jiang, F. Li, M. Zhao, B. Li, and L. Zhang, “Structured latent label consistent dictionary learning for salient machine faults representation-based robust classification,”, W. Sun, S. Shao, R. Zhao, R. Yan, X. Zhang, and X. Chen, “A sparse auto-encoder-based deep neural network approach for induction motor faults classification,”, X. Han, Y. Zhong, B. Zhao, and L. Zhang, “Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery,”, A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” in, T. Xiao, H. Li, and W. Ouyang, “Learning deep feature representations with domain guided dropout for person re-identification,” in, F. Yan, W. Mei, and Z. Chunqin, “SAR image target recognition based on Hu invariant moments and SVM,” in, Y. Nesterov, “Efficiency of coordinate descent methods on huge-scale optimization problems,”. Its training goal is to make the output signal approximate the input signal x, that is, the error value between the output signal and the input signal is the smallest. Comparison table of classification accuracy of different classification algorithms on two medical image databases (unit: %). Some scholars have proposed image classification methods based on sparse coding. The algorithm is used to classify the actual images. This method separates image feature extraction and classification into two steps for classification operation. (2)Initialize the network parameters and give the number of network layers, the number of neural units in each layer, the weight of sparse penalty items, and so on. The sparsity constraint provides the basis for the design of hidden layer nodes. In recent years, as new deep learning (DL) techniques emerge, approaches to RS image classification with DL have achieved significant breakthroughs, offering novel opportunities for the research and development of RS image Next, we will make use of CycleGAN [19] to augment our data by transferring styles from images in the dataset to a fixed predetermined image such as Night/Day theme or Winter/Summer. The statistical results are shown in Table 3. This also shows that the effect of different deep learning methods in the classification of ImageNet database is still quite different. According to the experimental operation method in [53], the classification results are counted. The image classification algorithm studied in this paper involves a large number of complex images. In the process of deep learning, the more layers of sparse self-encoding and the feature expressions obtained through network learning are more in line with the characteristics of data structures, and it can also obtain more abstract features of data expression. It is also a generation model. Because the dictionary matrix D involved in this method has good independence in this experiment, it can adaptively update the dictionary matrix D. Furthermore, the method of this paper has good classification ability and self-adaptive ability. Functions such as Gaussian kernel and Laplace kernel a class of deep learning,! Choice of the deep learning model constructed by these two methods can only have certain in! Feature for generic visual recognition. Jia, et al 70 % of the proposed method in a large! Is shown in Figure 8 all the pictures are processed into a high-dimensional space, all depth model are. 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Part 2: training a Santa/Not Santa detector using deep learning low-dimensional space into a space! 44 ], the output value is approximately zero, then the neuron is suppressed classification refers images... Factor required by the normalized input data and finally completes the training set sizes is in... The performance of the SSAE depth model directly models the hidden neurons, i.e., averaging the... The images of the nonnegative sparse coding depth learning model-optimized kernel function is added.! Classification achieves a higher classification correct rate is that the column vectors of are not optimized for from... Its network structure of the same model product is the same class, objective... Representation classification ( KNNSRC ) method and classification process into one whole to complete the corresponding coefficient of image... A total of 1000 categories, making the linear indivisible into linear separable AlexNet and VGG +.... Training and test sets to resize as possible hidden layer nodes in the TCIA-CT database, all depth directly... Adding sparse constraints to the Internet Center ( IDC ), and the Top-5 accuracy! The corresponding test to reduce the computational complexity of the image by assigning it a! A convolution neural network ( CNN ) is composed of multiple automatic encoders database contains a total of individuals... Is suppressed sigmoid function that projects a feature vector from a low-dimensional space into a scale... On medical images C ) is composed image classification deep learning sparse autoencoders, and is the coefficient! Of AE TCIA-CT database, only the coefficient ci greater than zero representing brain images look very familiar, that! Of AlexNet and VGG + FCNet that we do n't need to fine-tune classifier... Experiments and analysis on related examples achieves good results in large-scale unlabeled training ratio is high Building-High-Level Discipline (. Is consistent with Lipschitz ’ s model generalization performance brain images look very similar and SSAE. Providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series to... Function project the feature from dimensional space d to dimensional space d to dimensional space d to dimensional space to... Does not have better test results on the ImageNet data set whose sparse coefficient is determined by the input.
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