Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods. * Corresponding author: Yan Pan, email: panyan5@mail.sysu.edu.cn. posed, e.g., [8,9,4,12,16,27,14,25,3]. The existing learning-based hashing methods can be categorized into unsupervised and supervised methods, based on whether supervised information (e.g., similarities or dissimilarities on data points) is involved. Compact bitwise representations are advantageous for improving the efficiency in both storage and search speed, particularly in big data applications. Compared to unsupervised methods, supervised methods usually embed the input data points into compact hash codes with fewer bits, with the help of supervised information.
A series of biodegradable PCL-PEG-PCL block copolymers were successfully synthesized by ring-opening polymerization of epsilon-caprolactone initiated by poly(ethylene glycol) (PEG), which were characterized by (1)H NMR, (13)C NMR, and FTIR. Their aqueous solution displayed special gel-sol transition behavior with temperature increasing from 4 to 100 degrees C, when the polymer concentration was above corresponding critical gel concentration (CGC). The gel-sol phase diagram was recorded using test tube inverting method and DSC method, which depended not only on chemical composition of copolymers, but also on heating history of copolymer's aqueous solution. As a result, the gel-sol transition temperature could be adjusted, which might be very useful for its application in biomedical fields such as injectable drug delivery system. And the typical shell-core structure of PCL-PEG-PCL micelles was introduced. The micelle-packing and partial crystallization might be the key gelation machanism for this gel-sol transition behavior of PCL-PEG-PCL aqueous solution.
Hashing is a popular approximate nearest neighbor search approach for large-scale image retrieval. Supervised hashing, which incorporates similarity/dissimilarity information on entity pairs to improve the quality of hashing function learning, has recently received increasing attention. However, in the existing supervised hashing methods for images, an input image is usually encoded by a vector of hand-crafted visual features. Such hand-crafted feature vectors do not necessarily preserve the accurate semantic similarities of images pairs, which may often degrade the performance of hashing function learning. In this paper, we propose a supervised hashing method for image retrieval, in which we automatically learn a good image representation tailored to hashing as well as a set of hash functions. The proposed method has two stages. In the first stage, given the pairwise similarity matrix $S$ over training images, we propose a scalable coordinate descent method to decompose $S$ into a product of $HH^T$ where $H$ is a matrix with each of its rows being the approximate hash code associated to a training image. In the second stage, we propose to simultaneously learn a good feature representation for the input images as well as a set of hash functions, via a deep convolutional network tailored to the learned hash codes in $H$ and optionally the discrete class labels of the images. Extensive empirical evaluations on three benchmark datasets with different kinds of images show that the proposed method has superior performance gains over several state-of-the-art supervised and unsupervised hashing methods.
Asphalt roads are the basic component of a land transportation system, and the quality of asphalt roads will decrease during the use stage because of the aging and deterioration of the road surface. In the end, some road pavement distresses may appear on the road surface, such as the most common potholes and cracks. In order to improve the efficiency of pavement inspection, currently some new forms of remote sensing data without destructive effect on the pavement are widely used to detect the pavement distresses, such as digital images, light detection and ranging, and radar. Multispectral imagery presenting spatial and spectral features of objects has been widely used in remote sensing application. In our study, the multispectral pavement images acquired by unmanned aerial vehicle (UAV) were used to distinguish between the normal pavement and pavement damages (e.g., cracks and potholes) using machine learning algorithms, such as support vector machine, artificial neural network, and random forest. Comparison of the performance between different data types and models was conducted and is discussed in this study, and indicates that a UAV remote sensing system offers a new tool for monitoring asphalt road pavement condition, which can be used as decision support for road maintenance practice. Index Terms-Artificial neural network (ANN), asphalt roads, multispectral imagery, pavement distress, random forest (RF), support vector machine (SVM), unmanned aerial vehicle (UAV). I. INTRODUCTIONR OADS, as one of the most important infrastructures, play a crucial role in supporting the development of an economy and human society. Therefore, it is imperative to maintain roads in a good condition. The quality of road pavement directly Manuscript
Multi-view clustering, which seeks a partition of the data inmultiple views that often provide complementary information to eachother, has received considerable attention in recent years. In reallife clustering problems, the data in each view may haveconsiderable noise. However, existing clustering methods blindlycombine the information from multi-view data with possiblyconsiderable noise, which often degrades their performance. In thispaper, we propose a novel Markov chain method for RobustMulti-view Spectral Clustering (RMSC). Our method has a flavor oflow-rank and sparse decomposition, where we firstly construct atransition probability matrix from each single view, and then usethese matrices to recover a shared low-rank transition probabilitymatrix as a crucial input to the standard Markov chain methodfor clustering. The optimization problem of RMSC has a low-rankconstraint on the transition probability matrix, and simultaneouslya probabilistic simplex constraint on each of its rows. To solvethis challenging optimization problem, we propose an optimization procedurebased on the Augmented Lagrangian Multiplier scheme. Experimentalresults on various real world datasets show that theproposed method has superior performance over severalstate-of-the-art methods for multi-view clustering.
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