Low-Rank Discriminative Least Squares Regression for Image Classification

Latest least squares regression (LSR) methods mainly try to learn slack regression targets to replace strict zero-one labels. However, the difference of intra-class targets can also be highlighted when enlarging the distance between different classes, and roughly persuing relaxed targets may lead to the problem of overfitting. To solve above problems, we propose a low-rank discriminative least squares regression model (LRDLSR) for multi-class image classification. Specifically, LRDLSR class-wisely imposes low-rank constraint on the intra-class regression targets to encourage its compactness and similarity. Moreover, LRDLSR introduces an additional regularization term on the learned targets to avoid the problem of overfitting. These two improvements are helpful to learn a more discriminative projection for regression and thus achieving better classification performance. Experimental results over a range of image databases demonstrate the effectiveness of the proposed LRDLSR method

An analysis of historical wind-driven rain loads for selected Canadian cities

Abstract The performance and durability of wall assemblies are greatly affected by the moisture load to which they may be subjected, in particular those arising from Wind-Driven Rain (WDR). Standard approaches for estimating such moisture loads assume 1% of the WDR load, whereas these loads have also been assessed from watertightness tests, although these assumed loads have been determined based on limited climate information. To more accurately estimate the moisture loads to which wall assemblies may be subjected over their service life, an analysis of historical WDR loads was completed for 11 cities across Canada. The magnitude, probability of occurrence of WDR loads in different cities and correlations between WDR related climate parameters, are discussed in this paper. Also, a novel WDR severity index is introduced, referred to as the Wind-Driven Rain Pressure Index, to permit quantifying the real-time and simultaneously occurring effects of WDR intensity and Driving Rain Wind Pressure (DRWP). To estimate the WDR intensity and DRWP with a specific probability of occurrence, an Extreme Value Analysis (EVA) was completed for a climate dataset of 31 years (1986–2016) using the Generalized Extreme Value and Gumbel distributions

Facile preparation of β-/γ-MgH2 nanocomposites under mild conditions and pathways to rapid dehydrogenation

A magnesium hydride composite with enhanced hydrogen desorption kinetics can be synthesized via a simple wet chemical route by ball milling MgH2 with LiCl as an additive at room temperature followed by tetrahydrofuran (THF) treatment under an Ar atmosphere. The as-synthesized composite comprises ca. 18 mass% orthorhombic γ-MgH2 and 80 mass% tetragonal β-MgH2 as submicron-sized particles. The β-/γ-MgH2 nanocomposite exhibits a dehydrogenation capacity of 6.6 wt.% and starts to release hydrogen at ~260 °C; ca. 140 °C lower than that of commercial MgH2. The apparent activation energy for dehydrogenation is 115±3 kJ mol-1, which is ca. 46 % lower than that of commercial MgH2. Analysis suggests that the meta-stable γ-MgH2 component either directly dehydrogenates exothermically or first transforms into stable β-MgH2 very close to the dehydrogenation onset. The improved hydrogen release performance can be attributed both to the existence of the MgH2 nanostructure and to the presence of γ-MgH2

Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

Discriminative localization is essential for fine-grained image classification task, which devotes to recognizing hundreds of subcategories in the same basic-level category. Reflecting on discriminative regions of objects, key differences among different subcategories are subtle and local. Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers. However, these methods generally have two limitations: (1) Separation of the two-stage learning is time-consuming. (2) Dependence on object and parts annotations for discriminative localization learning leads to heavily labor-consuming labeling. It is highly challenging to address these two important limitations simultaneously. Existing methods only focus on one of them. Therefore, this paper proposes the discriminative localization approach via saliency-guided Faster R-CNN to address the above two limitations at the same time, and our main novelties and advantages are: (1) End-to-end network based on Faster R-CNN is designed to simultaneously localize discriminative regions and encode discriminative features, which accelerates classification speed. (2) Saliency-guided localization learning is proposed to localize the discriminative region automatically, avoiding labor-consuming labeling. Both are jointly employed to simultaneously accelerate classification speed and eliminate dependence on object and parts annotations. Comparing with the state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach achieves both the best classification accuracy and efficiency.Comment: 9 pages, to appear in ACM MM 201