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Recently, person re-identification (PR-ID) has attracted numerous of research interest because of its broad applications. However, most of the existing PR-ID models always follow the supervised framework, which requires substantial labeled data. In fact, it is often very hard to get enough labeled training samples in many practical application scenarios. To overcome this limitation, some semi-supervised PR-ID methods have been presented more recently. Although some of these semi-supervised models achieve satisfied results, there is still much space to improve. In this paper, we propose a novel semi-supervised PR-ID by similarity-embedded cycle GANs (SECGAN). Our SECGAN model can learn cross-view features with limited labeled data by using cycle GANs. Simultaneously, to further enhance the ability of cycle GANs so that it can extract more discriminative and robust features, similarity metric subnet and specific features extracting subnet are embedded into cycle GANs. Extensive experiments have been conducted on three public PR-ID benchmark datasets, and the experimental results show that our proposed SECGAN approach outperforms several typical supervised methods and the existing state-of-the-art semi-supervised methods including traditional and deep learning semi-supervised methods. [ABSTRACT FROM AUTHOR]

Different views of one object usually represent different aspects of the object, and a single view is unlikely to comprehensively describe the object. In multi-view learning, comprehensive utilization of multi-view information is helpful. In this paper, we propose a novel supervised latent subspace learning method called multi-view intact discriminant space learning (MIDSL) by efficiently integrating complementary multi-view information of different views. MIDSL learns a latent intact discriminant space by employing Fisher discrimination criterion to fully use class label information, which can well guide exploiting useful discriminant information, of labeled training samples. MIDSL can simultaneously minimize the within-class scatter and maximize the between-class scatter of the feature representations of different objects in the learned latent intact discriminant space. Aiming to utilize unlabeled samples to help mining more useful information for better learning latent intact discriminant space, we extend MIDSL method in semi-supervised scenario and propose semi-supervised multi-view intact discriminant space learning (SMIDSL) method. We further extend MIDSL and SMIDSL methods by kernel technique and propose kernelized multi-view intact discriminant space learning (KMIDSL) and kernelized semi-supervised multi-view intact discriminant space learning (KSMIDSL) methods. Experimental results on Caltech 101, LFW, MNIST and RGB-D datasets demonstrate the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

In practice, huge amount of samples is readily available, while labeled samples are often very limited and too expensive to be easily obtained. Multi-view features usually reveal different types of traits of labeled and unlabeled samples. Semi-supervised multi-view learning is a learning paradigm designed to meet the requirement of learning from complementary information of multiple views of labeled and unlabeled samples. In this paper, we propose a semi-supervised multiple kernel intact discriminant space learning (SMKIDSL) method to discover latent intact feature representations for those samples. SMKIDSL employs correlation discriminant analysis and label regression to fully use class label information for enhancing the discriminant power of latent intact feature representations. In SMKIDSL, multi-view collaboration learning mechanism is utilized to efficiently integrate complementary information of multiple views, which enables optimal view being dominant in learning process. Besides, kernel technique is used to tackle nonlinear issue of original multi-view features for exploiting more discriminant information. Comprehensive experiments are conducted on Caltech 101, LFW, MNIST and RGB-D datasets. And the experimental results demonstrate the effectiveness and efficiency of our proposed method. The robustness of our method is also confirmed by those results. [ABSTRACT FROM AUTHOR]

Video-based person re-identification (re-id) has attracted a lot of research interest. When facing dramatic growth in new pedestrian videos, existing video-based person re-id methods usually need large quantities of labeled pedestrian videos to train a discriminative model. In practice, labeling large quantities of pedestrian videos is a costly and time-consuming task, which will limit the application of these methods in the real environment. Therefore, it is valuable and necessary to investigate how to learn a discriminative re-id model by using limited labeled training pedestrian videos. In this paper, we propose a semi-supervised cross-view projection-based dictionary learning (SCPDL) approach for video-based person re-id. Specifically, SCPDL jointly learns a pair of feature projection matrices and a pair of dictionaries by integrating the information contained in labeled and unlabeled pedestrian videos. With the learned feature projection matrices, the influence of variations within each video to the re-id can be reduced. With the learned dictionary pair, pedestrian videos from two different cameras can be converted into coding coefficients in a common representation space, such that the differences between different cameras can be bridged. In the learning process, the labeled pedestrian videos are used to ensure that the learned dictionaries have favorable discriminability; the large quantities of unlabeled pedestrian videos are used to ensure that SCPDL can better capture the variations between pedestrian videos, such that the learned dictionaries can own stronger representative capability. Experiments on two public pedestrian sequence data sets (iLIDS-VID and PRID 2011) demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

When there exist not enough historical defect data for building an accurate prediction model, semisupervised defect prediction (SSDP) and cross-project defect prediction (CPDP) are two feasible solutions. Existing CPDP methods assume that the available source data are well labeled. However, due to expensive human efforts for labeling a large amount of defect data, usually, we can only utilize the suitable unlabeled source data. We call CPDP in this scenario as cross-project semisupervised defect prediction (CSDP). Although some within-project semisupervised defect prediction (WSDP) methods have been developed in recent years, there still exists much room for improvement on prediction performance. In this paper, we aim to provide a unified and effective solution for both CSDP and WSDP problems. We introduce the semisupervised dictionary learning technique and propose a cost-sensitive kernelized semisupervised dictionary learning (CKSDL) approach. CKSDL can make full use of the limited labeled defect data and a large amount of unlabeled data in the kernel space. In addition, CKSDL considers the misclassification costs in the dictionary learning process. Extensive experiments on 16 projects indicate that CKSDL outperforms state-of-the-art WSDP methods, using unlabeled cross-project defect data can help improve the WSDP performance, and CKSDL generally obtains significantly better prediction performance than related SSDP methods in the CSDP scenario. [ABSTRACT FROM AUTHOR]

Subspace learning is an effective and widely used image feature extraction and classification technique. However, for the large-scale image recognition issue in real-world applications, many subspace learning methods often suffer from large computational burden. In order to reduce the computational time and improve the recognition performance of subspace learning technique under this situation, we introduce the idea of parallel computing which can reduce the time complexity by splitting the original task into several subtasks. We develop a parallel subspace learning framework. In this framework, we first divide the sample set into several subsets by designing two random data division strategies that are equal data division and unequal data division. These two strategies correspond to equal and unequal computational abilities of nodes under parallel computing environment. Next, we calculate projection vectors from each subset in parallel. The graph embedding technique is employed to provide a general formulation for parallel feature extraction. After combining the extracted features from all nodes, we present a unified criterion to select most distinctive features for classification. Under the developed framework, we separately propose supervised and unsupervised parallel subspace learning approaches, which are called parallel linear discriminant analysis (PLDA) and parallel locality preserving projection (PLPP). PLDA selects features with the largest Fisher scores by estimating the weighted and unweighted sample scatter, while PLPP selects features with the smallest Laplacian scores by constructing a whole affinity matrix. Theoretically, we analyze the time complexities of proposed approaches and provide the fundamental supports for applying random division strategies. In the experiments, we establish two real parallel computing environments and employ four public image and video databases as the test data. Experimental results demonstrate that the proposed approaches outperform several related supervised and unsupervised subspace learning methods, and significantly reduce the computational time. [ABSTRACT FROM PUBLISHER]

• MCGCN for the first time builds cross-modal graph and jointly learns modality-specific and modality-shared features for semi-supervised cross-modal hashing. • MCGCN provides a three-channel network architecture, including two modality-specific channels and a cross-modal channel to model cross-modal graph with heterogeneous image and text features. • To effectively reduce the modality gap, network training is guided by adversarial scheme. • MCGCN obtains state-of-the-art semi-supervised cross-modal hashing performance. Cross-modal hashing maps heterogeneous multimedia data into Hamming space for retrieving relevant samples across modalities, which has received great research interests due to its rapid retrieval and low storage cost. In real-world applications, due to high manual annotation cost of multi-media data, we can only make use of limited number of labeled data with rich unlabeled data. In recent years, several semi-supervised cross-modal hashing (SCH) methods have been presented. However, how to fully explore and jointly utilize the modality-specific (complementarity) and modality-shared (correlation) information for retrieval has not been well studied for existing SCH works. In this paper, we propose a novel SCH approach named Modality-specific and Cross-modal Graph Convolutional Networks (MCGCN). The network architecture contains two modality-specific channels and a cross-modal channel to learn modality-specific and shared representations for each modality, respectively. Graph convolutional network (GCN) is leveraged in these three channels to explore intra-modal and inter-modal similarity, and perform semantic information propagation from labeled data to unlabeled data. Modality-specific and shared representations for each modality are fused with attention scheme. To further reduce the modality gap, a discriminative model is designed, learning to classify the modality of representations, and network training is guided by adversarial scheme. Experiments on two widely used multi-modal datasets demonstrate MCGCN outperforms state-of-the-art semi-supervised/supervised cross-modal hashing methods. [ABSTRACT FROM AUTHOR]