Towards Effective Codebookless Model for Image Classification

The bag-of-features (BoF) model for image classification has been thoroughly studied over the last decade. Different from the widely used BoF methods which modeled images with a pre-trained codebook, the alternative codebook free image modeling method, which we call Codebookless Model (CLM), attracted little attention. In this paper, we present an effective CLM that represents an image with a single Gaussian for classification. By embedding Gaussian manifold into a vector space, we show that the simple incorporation of our CLM into a linear classifier achieves very competitive accuracy compared with state-of-the-art BoF methods (e.g., Fisher Vector). Since our CLM lies in a high dimensional Riemannian manifold, we further propose a joint learning method of low-rank transformation with support vector machine (SVM) classifier on the Gaussian manifold, in order to reduce computational and storage cost. To study and alleviate the side effect of background clutter on our CLM, we also present a simple yet effective partial background removal method based on saliency detection. Experiments are extensively conducted on eight widely used databases to demonstrate the effectiveness and efficiency of our CLM method

Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

Global covariance pooling in convolutional neural networks has achieved impressive improvement over the classical first-order pooling. Recent works have shown matrix square root normalization plays a central role in achieving state-of-the-art performance. However, existing methods depend heavily on eigendecomposition (EIG) or singular value decomposition (SVD), suffering from inefficient training due to limited support of EIG and SVD on GPU. Towards addressing this problem, we propose an iterative matrix square root normalization method for fast end-to-end training of global covariance pooling networks. At the core of our method is a meta-layer designed with loop-embedded directed graph structure. The meta-layer consists of three consecutive nonlinear structured layers, which perform pre-normalization, coupled matrix iteration and post-compensation, respectively. Our method is much faster than EIG or SVD based ones, since it involves only matrix multiplications, suitable for parallel implementation on GPU. Moreover, the proposed network with ResNet architecture can converge in much less epochs, further accelerating network training. On large-scale ImageNet, we achieve competitive performance superior to existing counterparts. By finetuning our models pre-trained on ImageNet, we establish state-of-the-art results on three challenging fine-grained benchmarks. The source code and network models will be available at http://www.peihuali.org/iSQRT-COVComment: Accepted to CVPR 201

Theoretical and experimental methods of dynamic clothing pressure performance

A dynamic pressure measuring system has been developed in this study. This system can be used for static pressure measuring, real-time dynamic pressure measuring and pressure fatigue analysis. A 3D geometric model on fabric deformation as well as mechanical behavior has been developed, which can be used for simulating the fabric elongation during dynamic pressing, and to deduce relationship between the press depth and fabric elongation. The process parameters of measuring system have been systematically estimated and analyzed. The range of press depth fixed for dynamic pressure measurement is found to be 58-115 mm, which responds to the fabric elongation from 10% to 40%. The press velocity at 100 mm/min is considered as the optional one for dynamic pressure measurement. Five repeated test cycles can be satisfied to assess the dynamic pressure fatigue performance quickly

An experimental study on fabric softness evaluation

Purpose &ndash; To examine a simple testing method of measuring the force to pull a fabric through a series of parallel pins to determine the fabric softness property.Design/methodology/approach &ndash; A testing system was setup for fabric pulling force measurements and the testing parameters were experimentally determined. The specific pulling forces were compared with the fabric assurance by simple testing (FAST) parameters and subjective softness ranking. Their correlations were also statistically analyzed.Findings &ndash; The fabric pulling force reflects the physical and surface properties of the fabrics measured by the FAST instrument and its ability to rank fabric softness appears to be close to the human hand response on fabric softness. The pulling force method can also distinguish the difference of fabrics knitted with different wool fiber contents.Research limitations/implications &ndash; Only 21 woven and three knitted fabrics were used for this investigation. More fabrics with different structures and finishes may be evaluated before the testing method can be put in practice.Practical implications &ndash; The testing method could be used for objective assessment of fabric softness.Originality/value &ndash; The testing method reported in this paper is a new concept in fabric softness measurement. It can provide objective specifications for fabric softness, thus should be valuable to fabric community. <br /

Bis{1-[(4-methyl­phen­yl)imino­meth­yl]-2-naphtho­lato-κ2 N,O}nickel(II)

In the title complex, [Ni(C18H14NO)2], the NiII ion lies on an inversion center and is coordinated in a slightly distorted square-planar environment. The 1-[(4-methyl­phen­yl)imino­meth­yl]-2-naphtho­late ligands are coordinated in a trans arrangement with respect to the N and O atoms. In the symmetry-unique ligand, the dihedral angle between the naphthalene ring system and the benzene ring of the methyl­phenyl group is 49.03 (7)°