Person Re-Identification (person re-ID) is an image retrieval task which identifies the same person in different camera views. Generally, a good person re-ID model requires a large dataset containing over 100000 images to reduce the risk of over-fitting. Most current handcrafted person re-ID datasets, however, are insufficient for training a learning model with high generalization ability. In addition, the lacking of images with various levels of occlusion is still remaining in most existing datasets. Motivated by these two problems, this paper proposes a new data augmentation method called Random Linear Interpolation that can enlarge the sizes of person re-ID datasets and improve the generalization ability of the learning model. The key enabler of our approach is generating fused images by interpolating pairs of original images. In other words, the innovation of the proposed approach is considering data augmentation between two random samples. Plenty of experimental results demonstrates that the proposed method is effective to improve baseline models. On Market1501 and DukeMTMC-reID datasets, our approach can achieve 92.71% and 82.19% rank-1 accuracy, respectively.
相似文献Person re-identification plays important roles in many practical applications. Due to various human poses, complex backgrounds and similarity of person clothes, person re-identification is still a challenging task. In this paper, we mainly focus on the robust and discriminative appearance feature representation and proposed a novel multi-appearance method for person re-identification. First, we proposed a deep feature fusion method and get the multi-appearance feature by combining two Convolutional Neural Networks. Then, in order to further enhance the representation of the appearance feature, the multi-part model was constructed by combining the whole body and the six body parts. Additionally, we optimized the feature extraction process by adding a pooling layer. Comprehensive and comparative experiments with the state-of-the-art methods over publicly available datasets demonstrated that the proposed method can get promising results.
相似文献In this paper, we propose a new feature selection method called kernel fisher discriminant analysis and regression learning based algorithm for unsupervised feature selection. The existing feature selection methods are based on either manifold learning or discriminative techniques, each of which has some shortcomings. Although some studies show the advantages of two-steps method benefiting from both manifold learning and discriminative techniques, a joint formulation has been shown to be more efficient. To do so, we construct a global discriminant objective term of a clustering framework based on the kernel method. We add another term of regression learning into the objective function, which can impose the optimization to select a low-dimensional representation of the original dataset. We use L2,1-norm of the features to impose a sparse structure upon features, which can result in more discriminative features. We propose an algorithm to solve the optimization problem introduced in this paper. We further discuss convergence, parameter sensitivity, computational complexity, as well as the clustering and classification accuracy of the proposed algorithm. In order to demonstrate the effectiveness of the proposed algorithm, we perform a set of experiments with different available datasets. The results obtained by the proposed algorithm are compared against the state-of-the-art algorithms. These results show that our method outperforms the existing state-of-the-art methods in many cases on different datasets, but the improved performance comes with the cost of increased time complexity.
相似文献In this work, we present a novel multi-scale feature fusion network (M-FFN) for image captioning task to incorporate discriminative features and scene contextual information of an image. We construct multi-scale feature fusion network by leveraging spatial transformation and multi-scale feature pyramid networks via feature fusion block to enrich spatial and global semantic information. In particular, we take advantage of multi-scale feature pyramid network to incorporate global contextual information by employing atrous convolutions on top layers of convolutional neural network (CNN). And, the spatial transformation network is exploited on early layers of CNN to remove intra-class variability caused by spatial transformations. Further, the feature fusion block integrates both global contextual information and spatial features to encode the visual information of an input image. Moreover, spatial-semantic attention module is incorporated to learn attentive contextual features to guide the captioning module. The efficacy of the proposed model is evaluated on the COCO dataset.
相似文献C3D has been widely used for video representation and understanding. However, it is performed on spatio-temporal contexts in a global view, which often weakens its capacity of learning local representation. To alleviate this problem, a concise and novel multi-layer feature fusion network with the cooperation of local and global views is introduced. For the current network, the global view branch is used to learn the core video semantics, while the local view branch is used to capture the contextual local semantics. Unlike traditional C3D model, the global view branch can only provide the big view branch with the most activated video features from a broader 3D receptive field. Via adding such shallow-view contexts, the local view branch can learn more robust and discriminative spatio-temporal representations for video classification. Thus we propose 3D convolutional networks with multi-layer-pooling selection fusion for video classification, the integrated deep global feature is combined with the information originated from shallow layer of local feature extraction networks, through the space-time pyramid pooling, adaptive pooling and attention pooling three different pooling units, different time–space feature information is obtained, and finally cascaded and used for classification. Experiments on the UCF-101 and HMDB-51 datasets achieve correct classification rate 95.0% and 72.2% respectively. The results show that the proposed 3D convolutional networks with multi-layer-pooling selection fusion has better classification performance.
相似文献Heterogeneous domain adaptation is a challenging problem due to the fact that it requires generalizing a learning model across training data and testing data with different distributions and features. To alleviate the difficulty of this task, most researchers usually perform some data preprocessing operations. However, such operations may lead to the loss of shareable information before domain adaptation. Moreover, most current work neglects the structural information between data, which is crucial for classification. To overcome the limitations mentioned above, we propose a novel algorithm, named as heterogeneous discriminative features learning and label propagation (HDL), which includes i) features learning with label consistency through two domain-specific projections, and ii) label propagation through exploiting structural information of data. Notably, each of the two sides reinforces each other. For each objective function, the corresponding analytical solutions are presented. Comprehensive experimental evidence on a large number of text categorization, image sclassification and text to image recognition datasets verifies the effectiveness and efficiency of the proposed approach over several state-of-the-art methods.
相似文献Hyperspectral images (HSIs) classification have aroused a great deal of attention recently due to their wide range of practical prospects in numerous fields. Spatial-spectral fusion feature is widely used in HSI classification to get better performance. These methods are mostly based on a simple linear addition with the combined hyper-parameter to fuse the spatial and spectral information. It is necessary to fuse the features in a more suitable method. To solve this problem, we propose a novel HSI classification approach based on Hybrid spatial-spectral feature in broad learning system (HSFBLS). First, we employ an adaptive weighted mean filter to obtain spatial feature. Computing the weights of spatial and spectral channels in hybrid module by two BLS and uniting them with a weighted linear function. Then, we fuse the spectral-spatial feature by sparse autoencoder to get weighted fusion feature as the feature nodes to classify HSI data in BLS. By a two-stage fusion of spatial and spectral information, it can increase the classification accuracy contrast to simple combination. Very satisfactory classification results on typical HSI datasets illustrate the availability of proposed HSFBLS. Moreover, HSFBLS also reduce training time greatly contrast to time-consuming network.
相似文献The multivariate time series often contain complex mixed inputs, with complex correlations between them. Detecting change points in multivariate time series is of great importance, which can find anomalies early and reduce losses, yet very challenging as it is affected by many complex factors, i.e., dynamic correlations and external factors. The performance of traditional methods typically scales poorly. In this paper, we propose Finder, a novel approach of change point detection via multivariate fusion attention networks. Our model consists of two key modules. First, in the time series prediction module, we employ multi-level attention networks based on the Transformer and integrate the external factor fusion component, achieving feature extraction and fusion of multivariate data. Secondly, in the change point detection module, a deep learning classifier is used to detect change points, improving efficiency and accuracy. Extensive experiments prove the superiority and effectiveness of Finder on two real-world datasets. Our approach outperforms the state-of-the-art methods by up to 10.50% on the F1 score.
相似文献As the basis of mid-level and high-level vision tasks, edge detection has great significance in the field of computer vision. Edge detection methods based on deep learning usually adopt the structure of the encoding-decoding network, among which the deep convolutional neural network is generally adopted in the encoding network, and the decoding network is designed by researchers. In the design of the encoding-decoding network, researchers pay more attention to the design of the decoding network and ignore the influence of the encoding network, which makes the existing edge detection methods have the problems of weak feature extraction ability and insufficient edge information extraction. To improve the existing methods, this work combines the information transmission mechanism of the retina/lateral geniculate nucleus with an edge detection network based on convolutional neural network and proposes a bionic feature enhancement network. It consists of a pre-enhanced network, an encoding network, and a decoding network. By simulating the information transfer mechanism of the retina/lateral geniculate nucleus, we designed the pre-enhanced network to enhance the ability of the encoding network to extract details and local features. Based on the hierarchical structure of the visual pathway and the integrated feature function of the inferior temporal (IT) cortex, we designed a novel feature fusion network as a decoding network. In a feature fusion network, a down-sampling enhancement module is introduced to boost the feature integration ability of the decoding network. Experimental results demonstrate that we achieve state-of-the-art performance on several available datasets.
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