Abstract
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.
Highlights
Sparse representation has its unique advantages in signal processing, image processing, computer vision, pattern recognition, and so on
sparse representation-based classifier (SRC) used the original training samples as dictionaries to represent the test samples linearly and calculated the sparse representation coefficients of the test samples. It used sparse representation coefficients and training samples to calculate all kinds of reconstruction residuals, so the test samples can be identified according to the minimum reconstruction residuals
Aiming at the application requirements of object recognition, we introduce deep features into adaptive joint sparse representation and propose D-AJSR, a data-lightweight classification framework
Summary
Sparse representation has its unique advantages in signal processing, image processing, computer vision, pattern recognition, and so on. Lu and Linghua proposed a face recognition method based on discriminant dictionary learning, which used a Gabor filter to learn the new dictionary and classified the images with sparse representation [6]. The sparse representation of the traditional dictionary introduced above mostly used traditional features, which cannot meet the requirement of high recognition rate in many cases. In view of these situations, we improve the traditional dictionary into an extended dictionary and use deep features as the atoms in the dictionary to propose the D-AJSR approach. At the same time, compared with artificial intelligence methods, D-AJSR can classify and recognize objects well with few training samples
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