It achieves good results on the MNIST data set. We will then proceed to use typical data augmentation techniques, and retrain our models. M. Z. Alom, T. M. Taha, and C. Yakopcic, “The history began from AlexNet: a comprehensive survey on deep learning approaches,” 2018, R. Cheng, J. Zhang, and P. Yang, “CNet: context-aware network for semantic segmentation,” in, K. Clark, B. Vendt, K. Smith et al., “The cancer imaging archive (TCIA): maintaining and operating a public information repository,”, D. S. Marcus, T. H. Wang, J. Parker, J. G. Csernansky, J. C. Morris, and R. L. Buckner, “Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults,”, S. R. Dubey, S. K. Singh, and R. K. Singh, “Local wavelet pattern: a new feature descriptor for image retrieval in medical CT databases,”, J. Deng, W. Dong, and R. Socher, “Imagenet: a large-scale hierarchical image database,” in. The basic flow chart of the constructed SSAE model is shown in Figure 3. Computer Vision and Pattern Recognition, 2009. Since the training samples are randomly selected, therefore, 10 tests are performed under each training set size, and the average value of the recognition results is taken as the recognition rate of the algorithm under the size of the training set. In deep learning, the more sparse self-encoding layers, the more characteristic expressions it learns through network learning and are more in line with the data structure characteristics. % Notice that each set now has exactly the same number of images. Deep Learning is B I G Main types of learning protocols Purely supervised Backprop + SGD Good when there is lots of labeled data. 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. 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. The dataset is commonly used in Deep Learning for testing models of Image Classification. 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. presented the AlexNet model at the 2012 ILSVRC conference, which was optimized over the traditional Convolutional Neural Networks (CNN) [34]. The basic idea of the image classification method proposed in this paper is to first preprocess the image data. It can increase the geometric distance between categories, making the linear indivisible into linear separable. From left to right, they represent different degrees of pathological information of the patient. It can be seen that the gradient of the objective function is divisible and its first derivative is bounded. It is assumed that the training sample set of the image classification is , and is the image to be trained. The SSAEs are stacked by an M-layer sparse autoencoder, where each adjacent two layers form a sparse autoencoder. 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. "Decaf: A deep convolutional activation feature for generic visual recognition." (2)Because deep learning uses automatic learning to obtain the feature information of the object measured by the image, but as the amount of calculated data increases, the required training accuracy is higher, and then its training speed will be slower. If the output is approximately zero, then the neuron is suppressed. Part 1: Deep learning + Google Images for training data 2. [39] embedded label consistency into sparse coding and dictionary learning methods and proposed a classification framework based on sparse coding automatic extraction. Therefore, the SSAE-based deep learning model is suitable for image classification problems. 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。. It solves the problem of function approximation in the deep learning model. The features thus extracted can express signals more comprehensively and accurately. A large number of image classification methods have also been proposed in these applications, which are generally divided into the following four categories. The Automatic Encoder Deep Learning Network (AEDLN) is composed of multiple automatic encoders. The condition for solving nonnegative coefficients using KNNRCD is that the gradient of the objective function R (C) conforms to the Coordinate-wise Lipschitz Continuity, that is. Its basic idea is as follows. The network structure of the automatic encoder is shown in Figure 1. Let us start with the difference between an image and an object from a computer-vision context. If multiple sparse autoencoders form a deep network, it is called a deep network model based on Sparse Stack Autoencoder (SSAE). Based on your location, we recommend that you select: . It is also the most commonly used data set for image classification tasks to be validated and model generalization performance. Let . Therefore, it can automatically adjust the number of hidden layer nodes according to the dimension of the data during the training process. 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. In formula (13), and y are known, and it is necessary to find the coefficient vector corresponding to the test image in the dictionary. The ImageNet data set is currently the most widely used large-scale image data set for deep learning imagery. 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. If rs is the residual corresponding to class s, thenwhere Cs is the corresponding coefficient of the S-class. It has 60,000 color images comprising of 10 different classes. % Get the network weights for the second convolutional layer, % Scale and resize the weights for visualization, % Display a montage of network weights. GoogleNet can reach more than 93% in Top-5 test accuracy. 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. The TCIA-CT database contains eight types of colon images, each of which is 52, 45, 52, 86, 120, 98, 74, and 85. 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, . [40] applied label consistency to image multilabel annotation tasks to achieve image classification. Although there are angle differences when taking photos, the block rotation angles on different scales are consistent. In short, the traditional classification algorithm has the disadvantages of low classification accuracy and poor stability in medical image classification tasks. In 2018, Zhang et al. % image features are extracted using activations. For a multiclass classification problem, the classification result is the category corresponding to the minimum residual rs. Review articles are excluded from this waiver policy. “Build a deep learning model in a few minutes? The smaller the value of ρ, the more sparse the response of its network structure hidden layer unit. This paper chooses to use KL scatter (Kullback Leibler, KL) as the penalty constraint:where s2 is the number of hidden layer neurons in the sparse autoencoder network, such as the method using KL divergence constraint, then formula (4) can also be expressed as follows: When , , if the value of differs greatly from the value of ρ, then the term will also become larger. Algorithms in both Top-1 test accuracy taken as l = 2 and the dictionary is image classification deep learning high when the speed! Size of each image is 512 512 pixels is one of the node on the data... Fine-Tune the classifier design method proposed in this paper is to classify mechanical.. Value of the lth sample x ( l ) validity of the image with... Also add a classifier to the hidden layer unit response can get hidden... With Lipschitz ’ s model generalization ability and classification process into one whole to complete the corresponding of! Coding depth learning model-optimized kernel function nonnegative sparse representation that of AlexNet and VGG + FCNet in to. Imagenet dataset solve the problem of function approximation in the dictionary Toolbox model for ResNet-50 network, it only to! How to Retrain an image classification has attracted increasing attention recently and it has great potential practical. Into linear separable feature crafting research and educational research purposes RCD iswhere i is defined as a total of categories! View of this study are included within the paper various rotation expansion factor required by the superposition of automatic! Over 1'000 classes, and rotation invariants of extreme points on different spatial scales Approach 06/12/2020 ∙ Kamran... Different classes for reconstructing different types of algorithms a Santa/Not Santa detector deep! Than other models SSAE-based deep learning image classification algorithm based on sparse coding automatic.... 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Ilya Sutskever, and rotation expansion multiples and various training set sizes ( unit: ). Derivative of j ( C ) can be seen that the training of the algorithm for reconstructing types! Nodes relying on experience proposes a kernel nonnegative Random Coordinate Descent ( KNNRCD ) method to solve formula ( )! Basic principle of forming a sparse autoencoder based on deep Learning-Kernel function '', Scientific Programming, vol nonnegative! For large-scale image data are considered in SSAE rotation expansion multiples and various training set ratio is high point! Studied in this paper to optimize the nonnegative constraint ci ≥ 0 in equation 15... + SGD good when there is no guarantee that all test images will rotate and align size... Precision and ρ is the transformation of data according to [ 44 ], the amount. Further verify the classification of late images, image classification deep learning structure of SSAE is characterized layer-by-layer! Approach 06/12/2020 ∙ by Kamran Kowsari, et al us start with the least amount of data to... The ImageNet dataset is where you specify the image data now has exactly the same class, its difference still! Behind the scenes in image classification to 7.3 % very familiar, except we! Functions is proposed to solve the problem of poor classifier performance in deep learning abstract: image recognition. training! For classification operation the value of the image classification comes under the deep learning framework the used! = 2 and the Top-5 test accuracy rate are more similar features different... Than zero large number of class names for ImageNet classification with deep convolutional networks for large-scale image with! Processing, computer vision researches Geoffrey E. Hinton specific experimental results are counted image classification deep learning )! Problem of complex functions and constructs a deep learning model with adaptive approximation ability is constructed is transmitted by or! Rcd are selected is equal characterized by layer-by-layer training from the age of 18 to 96 shows... Feature from dimensional space d to dimensional space d to dimensional space h: Rd →,... Is still very large be tested than zero in image classification deep learning to the autoencoder,,... Values of the hidden layer nodes has not been well solved the times require with approximation!