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image classification deep learning

Algorithms under Deep Learning process information the same way the human brain does, but obviously on a very small scale, since our brain is too complex (our brain has around 86 billion neurons). Therefore, if you want to achieve data classification, you must also add a classifier to the last layer of the network. 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. Its basic idea is as follows. It is defined as the task of classifying an image from a … In this section, the experimental analysis is carried out to verify the effect of the multiple of the block rotation expansion on the algorithm speed and recognition accuracy, and the effect of the algorithm on each data set. The classification algorithm proposed in this paper and other mainstream image classification algorithms are, respectively, analyzed on the abovementioned two medical image databases. (2) Image classification methods based on traditional colors, textures, and local features: the typical feature of local features is scale-invariant feature transform (SIFT). A large number of image classification methods have also been proposed in these applications, which are generally divided into the following four categories. This strategy leads to repeated optimization of the zero coefficients. It can be seen from Table 3 that the image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation is compared with the traditional classification algorithm and other depth algorithms. Image Classification – Deep Learning Project in Python with Keras. At the same time, combined with the practical problem of image classification, this paper proposes a deep learning model based on the stacked sparse autoencoder. , ci ≥ 0,  ≥ 0. It defines a data set whose sparse coefficient exceeds the threshold as a dense data set. At present, computer vision technology has developed rapidly in the field of image classification [1, 2], face recognition [3, 4], object detection [5–7], motion recognition [8, 9], medicine [10, 11], and target tracking [12, 13]. [5] Tensorflow: How to Retrain an Image Classifier for New Categories. Although the deep learning theory has achieved good application results in image classification, it has problems such as excessive gradient propagation path and over-fitting. We can see… Jun-e Liu, Feng-Ping An, "Image Classification Algorithm Based on Deep Learning-Kernel Function", Scientific Programming, vol. In order to achieve the purpose of sparseness, when optimizing the objective function, those which deviate greatly from the sparse parameter ρ are punished. The sparse penalty item only needs the first layer parameter to participate in the calculation, and the residual of the second hidden layer can be expressed as follows: After adding a sparse constraint, it can be transformed intowhere is the input of the activation amount of the Lth node j, . The SSAE depth model directly models the hidden layer response of the network by adding sparse constraints to the deep network. SSAE’s model generalization ability and classification accuracy are better than other models. [41] proposed a valid implicit label consistency dictionary learning model to classify mechanical faults. The ImageNet data set is currently the most widely used large-scale image data set for deep learning imagery. "Very deep convolutional networks for large-scale image recognition." [39] embedded label consistency into sparse coding and dictionary learning methods and proposed a classification framework based on sparse coding automatic extraction. The latter three corresponding deep learning algorithms can unify the feature extraction and classification process into one whole to complete the corresponding test. Finally, this paper uses the data enhancement strategy to complete the database, and obtains a training data set of 988 images and a test data set of 218 images. Due to the constraints of sparse conditions in the model, the model has achieved good results in large-scale unlabeled training. It has 60,000 color images comprising of 10 different classes. Therefore, can be used to represent the activation value of the input vector x for the first hidden layer unit j, then the average activation value of j is. At the same time, combined with the basic problem of image classification, this paper proposes a deep learning model based on the stacked sparse autoencoder. Finally, the full text is summarized and discussed. It can efficiently learn more meaningful expressions. In order to reflect the performance of the proposed algorithm, this algorithm is compared with other mainstream image classification algorithms. presented the AlexNet model at the 2012 ILSVRC conference, which was optimized over the traditional Convolutional Neural Networks (CNN) [34]. The classification accuracy of the three algorithms corresponding to other features is significantly lower. Image classification with deep learning most often involves convolutional neural networks, or CNNs. In Figure 1, the autoencoder network uses a three-layer network structure: input layer L1, hidden layer L2, and output layer L3. 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. In general, the dimensionality of the image signal after deep learning analysis increases sharply and many parameters need to be optimized in deep learning. In DNN, the choice of the number of hidden layer nodes has not been well solved. Advances in neural information processing systems. This is because the completeness of the dictionary is relatively high when the training set is high. The basic principle of forming a sparse autoencoder after the automatic encoder is added to the sparse constraint as follows. is where you specify the image size, which, in this case, is 28-by-28-by-1. Therefore, it can get a hidden layer sparse response, and its training objective function is. It solves the approximation problem of complex functions and constructs a deep learning model with adaptive approximation ability. [32] proposed a Sparse Restricted Boltzmann Machine (SRBM) method. **Image Classification** is a fundamental task that attempts to comprehend an entire image as a whole. These large numbers of complex images require a lot of data training to dig into the deep essential image feature information. The SSAE model proposed in this paper is a new network model architecture under the deep learning framework. Because although this method is also a variant of the deep learning model, the deep learning model proposed in this paper has solved the problems of model parameter initialization and classifier optimization. The SSAE is implemented by the superposition of multiple sparse autoencoders, and the SSAE is the same as the deep learning model. However, the classification accuracy of the depth classification algorithm in the overall two medical image databases is significantly better than the traditional classification algorithm. To achieve the goal of constraining each neuron, usually ρ is a value close to 0, such as ρ = 0.05, i.e., only 5% chance is activated. 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. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. % Get the network weights for the second convolutional layer, % Scale and resize the weights for visualization, % Display a montage of network weights. If the number of hidden nodes is more than the number of input nodes, it can also be automatically coded. The number of hidden layer nodes in the self-encoder is less than the number of input nodes. At the same time, the performance of this method in both medical image databases is relatively stable, and the classification results are also very accurate. The block size and rotation expansion factor required by the algorithm for reconstructing different types of images are not fixed. Therefore, the activation values of all the samples corresponding to the node j are averaged, and then the constraints arewhere ρ is the sparse parameter of the hidden layer unit. For the first time in the journal science, he put forward the concept of deep learning and also unveiled the curtain of feature learning. Deep learning allows machines to … To this end, this paper uses the setting and classification of the database in the literature [26, 27], which is divided into four categories, each of which contains 152, 121, 88, and 68 images. The class to be classified is projected as , and the dictionary is projected as . In the case where the proportion of images selected in the training set is different, there are certain step differences between AlexNet and VGG + FCNet, which also reflects the high requirements of the two models for the training set. Deep Learning is B I G Main types of learning protocols Purely supervised Backprop + SGD Good when there is lots of labeled data. % Number of class names for ImageNet classification task, % Create augmentedImageDatastore from training and test sets to resize. Moreover, the weight of its training is more in line with the characteristics of the data itself than the traditional random initialization method, and the training speed is faster than the traditional method. The network structure of the automatic encoder is shown in Figure 1. In 2013, the National Cancer Institute and the University of Washington jointly formed the Cancer Impact Archive (TCIA) database [51]. Y. Wei, W. Xia, M. Lin et al., “Hcp: a flexible cnn framework for multi-label image classification,”, T. Xiao, Y. Xu, and K. Yang, “The application of two-level attention models in deep convolutional neural network for fine-grained image classification,” in, F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: a unified embedding for face recognition and clustering,” in, C. Ding and D. Tao, “Robust face recognition via multimodal deep face representation,”, S. Ren, K. He, R. Girshick, and J. It enhances the image classification effect. Specifically, image classification comes under the computer vision project category. Therefore, when identifying images with a large number of detail rotation differences or partial random combinations, it must rotate the small-scale blocks to ensure a high recognition rate. Compared with the deep belief network model, the SSAE model is simpler and easier to implement. It can reduce the size of the image signal with large structure and complex structure and then layer the feature extraction. 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. Then, a sparse representation classifier for optimizing kernel functions is proposed to solve the problem of poor classifier performance in deep learning models. Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey Görkem Algan, Ilkay Ulusoy Image classification systems recently made a big leap with the advancement of deep neural networks. From left to right, the images of the differences in pathological information of the patient's brain image. Among them, the image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation is compared with DeepNet1 and DeepNet3. It reduces the Top-5 error rate for image classification to 7.3%. GoogleNet can reach more than 93% in Top-5 test accuracy. In DNN, the choice of the number of hidden layer nodes has not been well solved. The algorithm is used to classify the actual images. Comparison table of classification accuracy of different classification algorithms on two medical image databases (unit: %).

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