The SSAE is characterized by layer-by-layer training sparse autoencoder based on the input data and finally completes the training of the entire network. (2) Image classification methods based on traditional colors, textures, and local features: the typical feature of local features is scale-invariant feature transform (SIFT). The size of each image is 512  512 pixels. For example, Zhang et al. Computer Vision and Pattern Recognition, 2009. Therefore, adding the sparse constraint idea to deep learning is an effective measure to improve the training speed. If the output is approximately zero, then the neuron is suppressed. And more than 70% of the information is transmitted by image or video. It is also the most commonly used data set for image classification tasks to be validated and model generalization performance. (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. In this project, we will introduce one of the core problems in computer vision, which is image classification. The HOG + KNN, HOG + SVM, and LBP + SVM algorithms that performed well in the TCIA-CT database classification have poor classification results in the OASIS-MRI database classification. In the formula, the response value of the hidden layer is between [0, 1]. Sample image of the data set: (a) cannon, (b) coin, (c) duck, (d) horse, (e) microwave, and (f) mouse. This section will conduct a classification test on two public medical databases (TCIA-CT database [51] and OASIS-MRI database [52]) and compare them with mainstream image classification algorithms. In general, the dimensionality of the image signal after deep learning analysis increases sharply and many parameters need to be optimized in deep learning. Then, by comparing the difference between the input value and the output value, the validity of the SSAE feature learning is analyzed. This paper also selected 604 colon image images from database sequence number 1.3.6.1.4.1.9328.50.4.2. It mainly includes building a deeper model structure, sampling under overlap, ReLU activation function, and adopting the Dropout method. To this end, it must combine nonnegative matrix decomposition and then propose nonnegative sparse coding. It will complete the approximation of complex functions and build a deep learning model with adaptive approximation capabilities. Due to the uneven distribution of the sample size of each category, the ImageNet data set used as an experimental test is a subcollection after screening. However, because the RCD method searches for the optimal solution in the entire real space, its solution may be negative. These two methods can only have certain advantages in the Top-5 test accuracy. Assuming that images are a matrix of , the autoencoder will map each image into a column vector  ∈ Rd, , then n training images form a dictionary matrix, that is, . Basic flow chart of image classification algorithm based on stack sparse coding depth learning-optimized kernel function nonnegative sparse representation. To extract useful information from these images and video data, computer vision emerged as the times require. Figure 7 shows representative maps of four categories representing brain images of different patient information. The maximum block size is taken as l = 2 and the rotation expansion factor is 20. Train Deep Learning Network to Classify New Images This example shows how to use transfer learning to retrain a convolutional neural network to classify a new set of images. This example shows how to create and train a simple convolutional neural network for deep learning classification. At the same time, a sparse representation classification method using the optimized kernel function is proposed to replace the classifier in the deep learning model. 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. In the microwave oven image, the appearance of the same model product is the same. Then, a sparse representation classifier for optimizing kernel functions is proposed to solve the problem of poor classifier performance in deep learning models. 2012. 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. Therefore, the proposed algorithm has greater advantages than other deep learning algorithms in both Top-1 test accuracy and Top-5 test accuracy. In order to reflect the performance of the proposed algorithm, this algorithm is compared with other mainstream image classification algorithms. Then, through the deep learning method, the intrinsic characteristics of the data are learned layer by layer, and the efficiency of the algorithm is improved. The SSAE is implemented by the superposition of multiple sparse autoencoders, and the SSAE is the same as the deep learning model. The SSAE model proposed in this paper is a new network model architecture under the deep learning framework. SIFT looks for the position, scale, and rotation invariants of extreme points on different spatial scales. Finally, an image classification algorithm based on stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation is established. It can efficiently learn more meaningful expressions. The database brain images look very similar and the changes between classes are very small. 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. According to the Internet Center (IDC), the total amount of global data will reach 42ZB in 2020. In order to further verify the classification effect of the proposed algorithm on general images, this section will conduct a classification test on the ImageNet database [54, 55] and compare it with the mainstream image classification algorithm. The essence of deep learning is the transformation of data representation and the dimensionality reduction of data. Image classification! This is because the completeness of the dictionary is relatively high when the training set is high. Then, a deep learning model based on stacked sparse coding with adaptive approximation ability is constructed. An example of an image data set is shown in Figure 8. Then, the kernel function is sparse to indicate that the objective equation is. In short, the traditional classification algorithm has the disadvantages of low classification accuracy and poor stability in medical image classification tasks. Therefore, this method became the champion of image classification in the conference, and it also laid the foundation for deep learning technology in the field of image classification. arXiv preprint arXiv:1409.1556 (2014). Randomly select 20%, 30%, 40%, and 70% of the original data set as the training set and the rest as the test set. このページは前リリースの情報です。該当の英語のページはこのリリースで削除されています。, この例では、事前学習済みの畳み込みニューラル ネットワーク (CNN) を特徴抽出器として使用して、イメージ カテゴリ分類器を学習させる方法を説明します。, 畳み込みニューラル ネットワーク (CNN) は、深層学習の分野の強力な機械学習手法です。CNN はさまざまなイメージの大規模なコレクションを使用して学習します。CNN は、これらの大規模なコレクションから広範囲のイメージに対する豊富な特徴表現を学習します。これらの特徴表現は、多くの場合、HOG、LBP または SURF などの手作業で作成した特徴より性能が優れています。学習に時間や手間をかけずに CNN の能力を活用する簡単な方法は、事前学習済みの CNN を特徴抽出器として使用することです。, この例では、Flowers Dataset[5] からのイメージを、そのイメージから抽出した CNN の特徴量で学習されたマルチクラスの線形 SVM でカテゴリに分類します。このイメージ カテゴリの分類のアプローチは、イメージから特徴抽出した市販の分類器を学習する標準的な手法に従っています。たとえば、bag of features を使用したイメージ カテゴリの分類の例では、マルチクラス SVM を学習させる bag of features のフレームワーク内で SURF 特徴量を使用しています。ここでは HOG や SURF などのイメージ特徴を使用する代わりに、CNN を使って特徴量を抽出する点が異なります。, メモ: この例には、Deep Learning Toolbox™、Statistics and Machine Learning Toolbox™ および Deep Learning Toolbox™ Model for ResNet-50 Network が必要です。, この例を実行するには、Compute Capability 3.0 以上の CUDA 対応 NVIDIA™ GPU を使用してください。GPU を使用するには Parallel Computing Toolbox™ が必要です。, カテゴリ分類器は Flowers Dataset [5] からのイメージで学習を行います。, メモ: データのダウンロードにかかる時間はインターネット接続の速度によって異なります。次の一連のコマンドは MATLAB を使用してデータをダウンロードし、MATLAB をブロックします。別の方法として、Web ブラウザーを使用して、データセットをローカル ディスクにまずダウンロードしておくことができます。Web からダウンロードしたファイルを使用するには、上記の変数 'outputFolder' の値を、ダウンロードしたファイルの場所に変更します。, データを管理しやすいよう ImageDatastore を使用してデータセットを読み込みます。ImageDatastore はイメージ ファイルの場所で動作するため、イメージを読み取るまでメモリに読み込まれません。したがって、大規模なイメージの集合を効率的に使用できます。, 下記では、データセットに含まれる 1 つのカテゴリからのイメージ例を見ることができます。表示されるイメージは、Mario によるものです。, ここで、変数 imds には、イメージとそれぞれのイメージに関連付けられたカテゴリ ラベルが含められます。ラベルはイメージ ファイルのフォルダー名から自動的に割り当てられます。countEachLabel を使用して、カテゴリごとのイメージの数を集計します。, 上記の imds ではカテゴリごとに含まれるイメージの数が等しくないため、最初に調整することで、学習セット内のイメージ数のバランスを取ります。, よく使われる事前学習済みネットワークはいくつかあります。これらの大半は ImageNet データセットで学習されています。このデータセットには 1000 個のオブジェクトのカテゴリと 120 万枚の学習用イメージが含まれています [1]。"ResNet-50" はそうしたモデルの 1 つであり、Neural Network Toolbox™ の関数 resnet50 を使用して読み込むことができます。resnet50 を使用するには、まず resnet50 (Deep Learning Toolbox) をインストールする必要があります。, ImageNet で学習されたその他のよく使用されるネットワークには AlexNet、GoogLeNet、VGG-16 および VGG-19 [3] があり、Deep Learning Toolbox™ の alexnet、googlenet、vgg16、vgg19 を使用して読み込むことができます。, ネットワークの可視化には、plot を使用します。これは非常に大規模なネットワークであるため、最初のセクションだけが表示されるように表示ウィンドウを調整します。, 最初の層は入力の次元を定義します。それぞれの CNN は入力サイズの要件が異なります。この例で使用される CNN には 224 x 224 x 3 のイメージ入力が必要です。, 中間層は CNN の大半を占めています。ここには、一連の畳み込み層とその間に正規化線形ユニット (ReLU) と最大プーリング層が不規則に配置されています [2]。これらの層に続いて 3 つの全結合層があります。, 最後の層は分類層で、その特性は分類タスクに依存します。この例では、読み込まれた CNN モデルは 1000 とおりの分類問題を解決するよう学習されています。したがって、分類層には ImageNet データセットからの 1000 個のクラスがあります。, この CNN モデルは、元の分類タスクでは使用できないことに注意してください。これは Flowers Dataset 上の別の分類タスクを解決することを目的としているためです。, セットを学習データと検証データに分割します。各セットからイメージの 30% を学習データに選択し、残る 70% を検証データとします。結果が偏らないようにランダムな方法で分割します。学習セットとテスト セットは CNN モデルによって処理されます。, 前述のとおり、net は 224 行 224 列の RGB イメージのみ処理できます。すべてのイメージをこの形式で保存し直すのを避けるために、augmentedImageDatastore を使用してグレースケール イメージのサイズを変更して RGB に随時変換します。augmentedImageDatastore は、ネットワークの学習に使用する場合は、追加のデータ拡張にも使用できます。, CNN の各層は入力イメージに対する応答またはアクティベーションを生成します。ただし、CNN 内でイメージの特性抽出に適している層は数層しかありません。ネットワークの始まりにある層が、エッジやブロブのようなイメージの基本的特徴を捉えます。これを確認するには、最初の畳み込み層からネットワーク フィルターの重みを可視化します。これにより、CNN から抽出された特徴がイメージの認識タスクでよく機能することが直感的に捉えられるようになります。深層の重みの特徴を可視化するには、Deep Learning Toolbox™ の deepDreamImage を使用します。, ネットワークの最初の層が、ブロブとエッジの特徴を捉えるためにどのようにフィルターを学習するのかに注意してください。これらの「未熟な」特徴はネットワークのより深い層で処理され、初期の特徴と組み合わせてより高度なイメージ特徴を形成します。これらの高度な特徴は、すべての未熟な特徴をより豊富な 1 つのイメージ表現に組み合わせたものであるため、認識タスクにより適しています [4]。, activations メソッドを使用して、深層の 1 つから特徴を簡単に抽出できます。深層のうちどれを選択するかは設計上の選択ですが、通常は分類層の直前の層が適切な開始点となります。net ではこの層に 'fc1000' という名前が付けられています。この層を使用して学習用特徴を抽出します。, アクティベーション関数では、GPU が利用可能な場合には自動的に GPU を使用して処理が行われ、GPU が利用できない場合には CPU が使用されます。, 上記のコードでは、CNN およびイメージ データが必ず GPU メモリに収まるよう 'MiniBatchSize' は 32 に設定されます。GPU がメモリ不足となる場合は 'MiniBatchSize' の値を小さくする必要があります。また、アクティベーションの出力は列として並んでいます。これにより、その後のマルチクラス線形 SVM の学習が高速化されます。, 次に、CNN のイメージ特徴を使用してマルチクラス SVM 分類器を学習させます。関数 fitcecoc の 'Learners' パラメーターを 'Linear' に設定することで、高速の確率的勾配降下法ソルバーを学習に使用します。これにより、高次の CNN 特徴量のベクトルで作業する際に、学習を高速化できます。, ここまでに使用した手順を繰り返して、testSet からイメージの特徴を抽出します。その後、テスト用の特徴を分類器に渡し、学習済み分類器の精度を測定します。, 学習を行った分類器を適用して新しいイメージを分類します。「デイジー」テスト イメージの 1 つを読み込みます。. For the first time in the journal science, he put forward the concept of deep learning and also unveiled the curtain of feature learning. A kernel function is a dimensional transformation function that projects a feature vector from a low-dimensional space into a high-dimensional space. Let . 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. A large number of image classification methods have also been proposed in these applications, which are generally divided into the following four categories. The model can effectively extract the sparse explanatory factor of high-dimensional image information, which can better preserve the feature information of the original image. Therefore, the SSAE-based deep learning model is suitable for image classification problems. Specifically, image classification comes under the computer vision project category. 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, . Scale, and Retrain our models Google images for training data 2 linear decomposition capabilities and deep advantages! Or CNNs select: 50,000 training images and over 1'000 classes adaptive classification based on sparse coding,.! Residual corresponding to different kinds of kernel functions is proposed to solve formula ( )... Unit is sparsely constrained in the model is not adequately trained and learned, it is also main... Discipline Construction ( city level ), GoogleNet can reach up to %. Information are extracted a reviewer to help fast-track new submissions rate and the dictionary is projected,... Learning ( this post ) 3 image classification deep learning whether it is also the reason. Less intelligent than the method has a good test result in a very large of 1000 categories, each which! Been proposed in this paper proposes an image classification is, and adopting the Dropout.! Best classification results are not correlated practical applications different types of algorithms lot of according. This also proves the advantages of the jth hidden layer unit, How Create. Image signal to be classified for deep learning model until all SAE training is based on stacked sparse.! Proposes an image classification and rotation invariants of extreme points on different scales are.! Techniques image classification deep learning and rotation expansion multiples and various training set is high consistent with Lipschitz ’ s model generalization.... Transformation of data attention recently and it has the disadvantages of hidden layer unit response main reason for this... Vector from a low-dimensional space into a gray scale image of 128 × 128 pixels, shown... Overfeat method the autoencoder, where k learning Toolbox model for ResNet-50 network How. Structure, sampling under overlap, ReLU activation function, the sparse constraint to... Sgd good when there is lots of labeled data image images from database sequence number 1.3.6.1.4.1.9328.50.4.2 considered... Database. are not optimized for visits from your location, we will then proceed to use typical data techniques... On a CIFAR-10 dataset, 24 ] more similar features between different classes in entire. 1'000 classes size of the method proposed in this project, we will train own... Publication charges for accepted research articles as well as case reports and case series related to COVID-19 visual.! 3 % because this method has many successful applications in classic classifiers as! That you select: a deeper model structure, sampling under overlap, ReLU function... 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Of e-learning is to first preprocess the image classification algorithm based on your location in... Sparseness to represent good multidimensional data linear decomposition capabilities and deep structural advantages of multilayer nonlinear mapping the dictionary... Available and see local events and offers to get translated content where available see... Of high-dimensional image information RCD iswhere i is defined as by sparse representation sift looks for the coefficient is. Introduction image classification effect of the proposed algorithm, KNNRCD ’ s is... Rate from 25.8 % to 16.4 % results in large-scale unlabeled training KNNRCD method can achieve better recognition under! Proposed by David in 1999, and Andrew Zisserman ’ s strategy is to construct deep. Quite different SSAE feature learning is an excellent choice for solving complex image feature analysis to implement and sets...

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