Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation

An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper. D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples. In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples. Then, we use the adaptive weighted joint sparse representation to identify the objects, in which the eigenvectors are reconstructed by calculating the contribution weights of each eigenvector. Aiming at the high-dimensional problem of deep features, we use the principal component analysis (PCA) method to reduce the dimensions. Lastly, combined with the joint sparse model, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on the joint feature dictionary, sparse representation-based classifier (SRC) is used to recognize the objects. Experiments on face images and remote sensing images show that D-AJSR is superior to the traditional SRC method and some other advanced methods.

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Image Target Recognition via Mixed Feature-Based Joint Sparse Representation

Computational Intelligence and Neuroscience ◽

10.1155/2020/8887453 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Xin Wang ◽

Can Tang ◽

Ji Li ◽

Peng Zhang ◽

Wei Wang

Keyword(s):

Sparse Representation ◽

Feature Vector ◽

Target Recognition ◽

Training Sample ◽

Gabor Wavelet ◽

Illumination Variation ◽

Training Samples ◽

Mixed Features ◽

Joint Sparse Representation ◽

Feature Dictionary

An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper. This method is robust to the illumination variation, deformation, and rotation of the target image. It is a data-lightweight classification framework, which can recognize targets well with few training samples. First, Gabor wavelet transform and convolutional neural network (CNN) are used to extract the Gabor wavelet features and deep features of training samples and test samples, respectively. Then, the contribution weights of the Gabor wavelet feature vector and the deep feature vector are calculated. After adaptive weighted reconstruction, we can form the mixed features and obtain the training sample feature set and test sample feature set. Aiming at the high-dimensional problem of mixed features, we use principal component analysis (PCA) to reduce the dimensions. Lastly, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on joint feature dictionary, the sparse representation based classifier (SRC) is used to recognize the targets. The experiments on different datasets show that this approach is superior to some other advanced methods.

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Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification

Complexity ◽

10.1155/2020/8516216 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Tianshi Wang ◽

Li Liu ◽

Huaxiang Zhang ◽

Long Zhang ◽

Xiuxiu Chen

Keyword(s):

Text Classification ◽

Real Data ◽

Training Sample ◽

Generative Adversarial Networks ◽

Adversarial Network ◽

Classification Framework ◽

Training Samples ◽

Text Classifiers ◽

Textual Features ◽

Public Datasets

With the continuous renewal of text classification rules, text classifiers need more powerful generalization ability to process the datasets with new text categories or small training samples. In this paper, we propose a text classification framework under insufficient training sample conditions. In the framework, we first quantify the texts by a character-level convolutional neural network and input the textual features into an adversarial network and a classifier, respectively. Then, we use the real textual features to train a generator and a discriminator so as to make the distribution of generated data consistent with that of real data. Finally, the classifier is cooperatively trained by real data and generated data. Extensive experimental validation on four public datasets demonstrates that our method significantly performs better than the comparative methods.

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Special Object Recognition Based on Sparse Representation in Multisource Data Fusion Samples

Mathematical Problems in Engineering ◽

10.1155/2020/4138746 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Changjun Zha

Keyword(s):

Object Recognition ◽

Data Fusion ◽

Sparse Representation ◽

Detection System ◽

Principal Component ◽

Redundant Data ◽

Limited Power ◽

Novel Method ◽

Different Sources ◽

Better Than

Wireless sensor networks (WSNs) suffer from limited power and large amounts of redundant data. This paper describes a multisource data fusion method for WSNs that can be combined with the characteristics of a profile detection system. First, principal component analysis is used to extract sample features and eliminate redundant information. Feature samples from different sources are then fused using a method of superposition to reduce the amount of data transmitted by the network. Finally, a mathematical model is proposed. On the basis of this model, a novel method of special object recognition based on sparse representation is developed for multisource data fusion samples according to the distribution of nonzero coefficients under an overcomplete dictionary. The experimental results from numerical simulations show that the proposed recognition method can effectively identify special objects in the fusion samples, and the overall performance is better than that of traditional methods.

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