At present, in the field of person reidentification (re-id), the commonly used supervised learning algorithms require a large amount of labeled samples, which is not conducive to the model promotion. On the other hand, the accuracy of unsupervised learning algorithms is lower than supervised algorithms due to the lack of discriminant information. To address these issues, we make use of a small amount of labeled samples to add discriminant information in the basic dictionary learning. Moreover, the sparse coefficients of dictionary learning are decomposed into a projection problem of the original features, and the projection matrix is trained by labeled samples, which is transformed into a metric learning problem. It thus integrates the advantages of the two methods through combining dictionary learning and metric learning. After the data are trained, a projection matrix is used to project the unlabeled features into a feature subspace and the labels of the samples are reconstructed. The semisupervised learning problem is then transformed to a supervised learning problem with a graph regularization term. Experiments on different public pedestrian datasets, such as VIPeR, PRID, iLIDS, and CUHK01, show that the recognition accuracy of our method is better than some other existing person re-id methods.
Focused on the issue that the person re-identification across non-overlapping camera views and the high dimensional features extracted from the images, a novel person re-identification algorithm is proposed. The algorithm obtained the semantic information of each camera view by the sparse learning, and then the Canonical Correlation Analysis (CCA) is used to carry out the high-level feature projection transformation. The algorithm aims to avoid the curse of dimensionality caused by the high dimensional feature operation via improving the feature matching ability. To the end, the characteristic distance between different views can be compared. The advantages of this method is to learn the robust pedestrian image feature representation and it also builds person re-identification model with block structure feature of pedestrian dataset, and the associated optimization problem is solved by utilizing the alternating directions framework in order to improve the performance of person re-identification. At last, the experimental results show that the proposed method has higher recognition efficiency on three benchmark datasets of the PRID 2011, iLIDS-VID and VIPeR.
At present, in the field of person re-identification, the commonly used supervised learning algorithms require a large size of labelled sample, which is not conducive to the model promotion. On the other hand, the accuracy of unsupervised learning algorithms is lower than supervised algorithms due to the lack of discriminant information. To address these issues in this paper, we make use of a small size of labelled sample to add discriminant information in the basic dictionary learning. Moreover, the sparse coefficients of dictionary learning are decomposed into a projection problem of the original features, and the projection matrix is trained by labelled samples, which is transformed into a metric learning problem. It thus integrates the advantages of the two methods through combining dictionary learning and metric learning. After the data is trained, a new projection matrix is used to project the unlabeled features into a new feature subspace and the labels of the samples are reconstructed. The semi-supervised learning problem is then transformed to a supervised learning problem with a Laplace term. Experiments on different public pedestrian datasets, such as VIPeR, PRID, iLIDS and CUHK01, show that the recognition accuracy of our method is better than some other existing person reidentification methods.
Affected by time difference, sensor and other unsure factors, there is often strip interfering appeared in bi-directional scanning satellite image. The usual method used to remove strip interfering is filtering in frequency field. According to the number of detecting units in detector, using properties of multi-scale and localization, a proper sub-image corresponding to strip noise is got by wavelet decomposition. By processing the sub-image, ideal result is obtained. As far as removing dragging-part and remaining information are concerned, this method is better than the one based on Fourier transform. Experiments with CBERS-1 images have shown that this method is very effective.
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