Xiaoke Zhu

Super-resolution Person re-identification with semi-coupled low-rank discriminant dictionary learning

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification. For the given training image set which consists of HR gallery and LR probe images, we aim to convert the features of LR images into discriminating HR features. Specifically, our approach learns a pair of HR and LR dictionaries and a mapping from the features of HR gallery images and LR probe images. To ensure that the converted features using the learned dictionaries and mapping have favorable discriminative capability, we design a discriminant term which requires the converted HR features of LR probe images should be close to the features of HR gallery images from the same person, but far away from the features of HR gallery images from different persons. In addition, we apply low-rank regularization in dictionary learning procedure such that the learned dictionaries can well characterize intrinsic feature space of HR and LR images. Experimental results on public datasets demonstrate the effectiveness of SLD2L.

Multi-spectral low-rank structured dictionary learning for face recognition

Multi-spectral face recognition has been attracting increasing interest. In the last decade, several multi-spectral face recognition methods have been presented. However, it has not been well studied that how to jointly learn effective features with favorable discriminability from multiple spectra even when multi-spectral face images are severely contaminated by noise. Multi-view dictionary learning is an effective feature learning technique, which learns dictionaries from multiple views of the same object and has achieved state-of-the-art classification results. In this paper, we for the first time introduce the multi-view dictionary learning technique into the field of multi-spectral face recognition and propose a multi-spectral low-rank structured dictionary learning (MLSDL) approach. It learns multiple structured dictionaries, including a spectrum-common dictionary and multiple spectrum-specific dictionaries, which can fully explore both the correlated information and the complementary information among multiple spectra. Each dictionary contains a set of class-specified sub-dictionaries. Based on the low-rank matrix recovery theory, we apply low-rank regularization in multi-spectral dictionary learning procedure such that MLSDL can well solve the problem of multi-spectral face recognition with high levels of noise. We also design the low-rank structural incoherence term for multi-spectral dictionary learning, so as to reduce the redundancy among multiple spectrum-specific dictionaries. In addition, to enhance the efficiency of classification procedure, we design a low-rank structured collaborative representation classification scheme for MLSDL. Experimental results on HK PolyU, CMU and UWA hyper-spectral face databases demonstrate the effectiveness of the proposed approach. We propose a multi-spectral low-rank structured dictionary learning approach.We learn spectrum-common dictionary and spectrum-specific dictionaries.Low-rank structured regularization and incoherence terms are designed.Low-rank structured collaborative representation classification is provided.

Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning

Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLD2L can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLD2L. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLD2L (MVSLD2L) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.

Distance learning by treating negative samples differently and exploiting impostors with symmetric triplet constraint for person re-identification

Distance learning (DL) is an effective technique for person reidentification (PR-ID). DL based methods learn the distance metric by exploiting the discriminative information contained in samples. In PR-ID, different types of negative samples own different amounts of discriminative information, and impostor samples usually own more than other well separable negative samples (WSN-samples). Therefore, how to make full use of the different discriminative information conveyed by all negative samples in the DL process is a critical issue to be investigated. In this paper, we propose a novel DL approach for PR-ID. Specifically, for each target sample, we divide its negative samples into impostors and WSN-samples. Then we learn the distance metric by utilizing impostors and WSN-samples differently. For impostors, we design a symmetric triplet constraint, which requires the impostor to be far away from both samples of its corresponding positive sample pair simultaneously; for WSN-samples, we require them to keep their favorable separability. Experimental results on three benchmark datasets demonstrate the effectiveness and efficiency of our approach.

Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction

Cross-project defect prediction (CPDP) refers to predicting defects in a target project using prediction models trained from historical data of other source projects. And CPDP in the scenario where source and target projects have different metric sets is called heterogeneous defect prediction (HDP). Recently, HDP has received much research interest. Existing HDP methods only consider the linear correlation relationship among the features (metrics) of the source and target projects, and such models are insufficient to evaluate nonlinear correlation relationship among the features. So these methods may suffer from the linearly inseparable problem in the linear feature space. Furthermore, existing HDP methods do not take the class imbalance problem into consideration. Unfortunately, the imbalanced nature of software defect datasets increases the learning difficulty for the predictors. In this paper, we propose a new cost-sensitive transfer kernel canonical correlation analysis (CTKCCA) approach for HDP. CTKCCA can not only make the data distributions of source and target projects much more similar in the nonlinear feature space, where the learned features have favorable separability, but also utilize the different misclassification costs for defective and defect-free classes to alleviate the class imbalance problem. We perform the Friedman test with Nemenyi’s post-hoc statistical test and the Cliff’s delta effect size test for the evaluation. Extensive experiments on 28 public projects from five data sources indicate that: (1) CTKCCA significantly performs better than the related CPDP methods; (2) CTKCCA performs better than the related state-of-the-art HDP methods.

On the Multiple Sources and Privacy Preservation Issues for Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) refers to predicting defect-proneness of software modules in a target project using heterogeneous metric data from other projects. Existing HDP methods mainly focus on predicting target instances with single source. In practice, there exist plenty of external projects. Multiple sources can generally provide more information than a single project. Therefore, it is meaningful to investigate whether the HDP performance can be improved by employing multiple sources. However, a precondition of conducting HDP is that the external sources are available. Due to privacy concerns, most companies are not willing to share their data. To facilitate data sharing, it is essential to study how to protect the privacy of data owners before they release their data. In this paper, we study the above two issues in HDP. Specifically, to utilize multiple sources effectively, we propose a multi-source selection based manifold discriminant alignment (MSMDA) approach. To protect the privacy of data owners, a sparse representation based double obfuscation algorithm is designed and applied to HDP. Through a case study of 28 projects, our results show that MSMDA can achieve better performance than a range of baseline methods. The improvement is 3.4-

Discriminant Tensor Dictionary Learning with Neighbor Uncorrelation for Image Set Based Classification.

15.3

Heterogeneous Defect Prediction Through Multiple Kernel Learning and Ensemble Learning

15.3 percent in g-measure and 3.0-

Software effort estimation based on open source projects: Case study of Github

19.1

Privacy preserving via interval covering based subclass division and manifold learning based bi-directional obfuscation for effort estimation

19.1 percent in AUC.