Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and extracted features please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda

Learning Discriminative Features with Class Encoder

Deep neural networks usually benefit from unsupervised pre-training, e.g. auto-encoders. However, the classifier further needs supervised fine-tuning methods for good discrimination. Besides, due to the limits of full-connection, the application of auto-encoders is usually limited to small, well aligned images. In this paper, we incorporate the supervised information to propose a novel formulation, namely class-encoder, whose training objective is to reconstruct a sample from another one of which the labels are identical. Class-encoder aims to minimize the intra-class variations in the feature space, and to learn a good discriminative manifolds on a class scale. We impose the class-encoder as a constraint into the softmax for better supervised training, and extend the reconstruction on feature-level to tackle the parameter size issue and translation issue. The experiments show that the class-encoder helps to improve the performance on benchmarks of classification and face recognition. This could also be a promising direction for fast training of face recognition models.Comment: Accepted by CVPR2016 Workshop of Robust Features for Computer Visio

Frequency and distribution of AP-1 sites in the human genome

The AP-1-binding sequences are promoter/enhancer elements that play an essential role in the induction of many genes in mammalian cells; however, the number of genes containing AP-1 sites remains unknown. In order to better address the overall effect of AP-1 on expression of genes encoded by the entire genome, a genome-wide analysis of the frequency and distribution of AP-1 sites would be useful; yet to date, no such analysis of AP-1 sites or any other promoter/enhancer elements has been performed. We present here our study of the consensus AP-1 site and two single-bp variants showing that the frequency of AP-1 sites in promoter regions is significantly lower than their average rate of occurrence in the whole genomic sequence, as well as the frequency of a random heptanucleotide suggesting that nature has selected for a decrease in the frequency of AP-1 sites in the regulatory regions of genes. In addition, genes containing multiple AP-1 sites are more prevalent than those containing only one copy of an AP-1 site, which again may have evolved to allow for greater signal amplification or integration in the regulation of AP-1 target genes. However, the number of AP-1-regulated genes identified in various studies is far smaller than the number of genes containing potential AP-1 sites, indicating that not all AP-1 sites are activated in a given cell under a given condition, and is consistent with the prediction by others that cellular context determines which AP-1 sites are targeted by AP-1

Face recognition using ordinal features

Abstract. In this paper, we present an ordinal feature based method for face recognition. Ordinal features are used to represent faces. Hamming distance of many local sub-windows is computed to evaluate differences of two ordinal faces. AdaBoost learning is finally applied to select most effective hamming distance based weak classifiers and build a powerful classifier. Experiments demonstrate good results for face recognition on the FERET database, and the power of learning ordinal features for face recognition.

Learning Multi-scale Block Local Binary Patterns for Face Recognition

Abstract. In this paper, we propose a novel representation, called Multiscale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It is more robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 database show that the proposed MB-LBP method significantly outperforms other LBP based face recognition algorithms. Keywords: LBP, MB-LBP, Face Recognition, AdaBoost.

Pressure-driven band gap engineering in ion-conducting semiconductor silver orthophosphate

The obtainment of active semiconductor photocatalysts remains a challenge for converting sunlight into clean fuels. A pressing need is to explore a novel method to tune the electronic band structures and gain insightful knowledge of the structure-property relationships. In this work, taking silver orthophosphate (Ag3PO4) as an example, a static pressure technique is applied to modulate the band gap and indirect-direct band character via altering its crystal structure and lattice parameters. Under ambient conditions, cubic Ag3PO4 possesses an indirect-band gap of similar to 2.4 eV. At elevated pressure, the band gap of Ag3PO4 narrowed from 2.4 eV to 1.8 eV, reaching the optimal value for efficient solar water splitting. During the pressure-induced structural evolution from cubic to trigonal phases, the indirect-to-direct band gap crossover was predicted by first-principles calculations combined with structure search and synchrotron X-ray diffraction experiments. Strikingly, the observed band gap narrowing was partially retained after releasing pressure to ambient pressure. This work paves an alternative pathway to engineer the electronic structure of semiconductor photocatalysts and design better photo-functional materials