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Cuckoo search with varied scaling factor

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Frontiers of Computer Science 2015年第4期9卷 623-635页

作者： Lijin WANG yilong yin Yiwen ZHONG School of Computer Science and Technology Shandong University Jinan 250101 China College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou 350002 China School of Computer Science and Technology Shandong University of Finance and Economics Jinan 250014 China

Cuckoo search （CS）, inspired by the obligate brood parasitic behavior of some cuckoo species, iteratively uses L6vy flights random walk （LFRW） and biased/selective random walk （BSRW） to search for new solutions. In this study, we seek a simple strategy to set the scaling factor in LFRW, which can vary the scaling factor to achieve better performance. However, choosing the best scaling factor for each problem is intractable. Thus, we propose a varied scal- ing factor （VSF） strategy that samples a value from the range [0,1] uniformly at random for each iteration. In addition, we integrate the VSF strategy into several advanced CS vari- ants. Extensive experiments are conducted on three groups of benchmark functions including 18 common test functions, 25 functions proposed in CEC 2005, and 28 functions intro- duced in CEC 2013. Experimental results demonstrate the ef- fectiveness of the VSF strategy.

关键词： cuckoo search algorithm uniform distribution,random sampling scaling factor function optimization prob-lems

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Fractional-order Sparse Representation for Image Denoising

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IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 555-563页

作者： Leilei Geng Zexuan Ji Yunhao Yuan yilong yin School of Computer Science and Technology Shandong University of Finance and Economics School of Computer Science and Engineering Nanjing University of Science and Technology School of Internet of Things Engineering Jiangnan University

Sparse representation models have been shown promising results for image denoising. However, conventional sparse representation-based models cannot obtain satisfactory estimations for sparse coefficients and the dictionary. To address this weakness, in this paper, we propose a novel fractional-order sparse representation(FSR) model. Specifically, we cluster the image patches into K groups, and calculate the singular values for each clean/noisy patch pair in the wavelet domain. Then the uniform fractional-order parameters are learned for each *** a novel fractional-order sample space is constructed using adaptive fractional-order parameters in the wavelet domain to obtain more accurate sparse coefficients and dictionary for image denoising. Extensive experimental results show that the proposed model outperforms state-of-the-art sparse representation-based models and the block-matching and 3D filtering algorithm in terms of denoising performance and the computational efficiency.

关键词： Index Terms-Fractional-order image denoising multi-scale sparse representation.

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Finger vein recognition based on deformation information

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Science China(Information Sciences) 2018年第5期61卷 156-170页

作者： Xianjing MENG Xiaoming XI Gongping YANG yilong yin School of Computer Science and Technology Shandong University of Finance and Economics School of Computer Science and Technology Shandong University

The measurement of the vessel pattern in fingers is a superior method for identifying individuals owing to its convenience and the security it offers. We introduce in this paper a new perspective to accomplish finger vein recognition. This method, which regards deformations as discriminative information, is distinct from existing methods that attempt to prevent the influence of deformations. The proposed technique is based on the observation that regular deformation, which corresponds to a posture change, can only exist in genuine vein patterns. In terms of methodology, we incorporate optimized matching to generate pixelbased 2 D displacements that correspond to deformations. The texture of uniformity extracted from the displacement fields is taken as the final matching score. Evaluated on two publicly available databases,Poly U and SDU-MLA, extensive experiments demonstrated that the discriminability of the new feature derived from deformations is preferable. The equal error rate(EER) achieved is the lowest compared to that of state-of-the-art techniques.

关键词： finger vein recognition deformations optimized matching displacement texture of uniformity

Anchor-based manifold binary pattern for finger vein recognition

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Science China(Information Sciences) 2019年第5期62卷 129-144页

作者： Haiying LIU Gongping YANG Lu YANG Kun SU yilong yin School of Software Shandong University School of Computer Science and Technology Shandong University of Finance and Economics

This paper proposes a novel learning method of binary local features for recognition of the finger vein. The learning methods existing in local features for image recognition intend to maximize the data variance, reduce quantitative errors, exploit the contextual information within each binary code, or utilize the label information, which all ignore the local manifold structure of the original data. The manifold structure actually plays a very important role in binary code learning, but constructing a similarity matrix for large-scale datasets involves a lot of computational and storage cost. The study attempts to learn a map, which can preserve the manifold structure between the original data and the learned binary codes for large-scale situations. To achieve this goal, we present a learning method using an anchor-based manifold binary pattern(AMBP) for finger vein recognition. Specifically, we first extract the pixel difference vectors(PDVs) in the local patches by calculating the differences between each pixel and its neighbors. Second,we construct an asymmetric graph, on which each data point can be a linear combination of its K-nearest neighbor anchors, and the anchors are randomly selected from the training samples. Third, a feature map is learned to project these PDVs into low-dimensional binary codes in an unsupervised manner, where(i) the quantization loss between the original real-valued vectors and learned binary codes is minimized and(ii) the manifold structure of the training data is maintained in the binary space. Additionally, the study fuses the discriminative binary descriptor and AMBP methods at the image representation level to further boost the performance of the recognition system. Finally, experiments using the MLA and PolyU databases show the effectiveness of our proposed methods.

关键词： finger vein recognition feature learning local linear embedding fusion manifold learning anchor

Non-negative locality-constrained vocabulary tree for finger vein image retrieval

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Frontiers of Computer Science 2019年第2期13卷 318-332页

作者： Kun SU Gongping YANG Lu YANG Peng SU yilong yin School of Computer Science and Technology Shandong University Jinan 250101 China School of Mechanical Electrical and Information Engineering Shandong University (Weihai)Weihai 264209 China School of Computer Science and Tech no logy Shandong University of Finance and Econo mics Jinan 250014 China School of Mathematics Dali University Dali 671000 China

Finger vein image retrieval is a biometric identification technology that has recently attracted a lot of attention. It has the potential to reduce the search space and has attracted a considerable amount of research effort recently. It is a challenging problem owing to the large number of images in biometric databases and the lack of efficient retrieval schemes. We apply a hierarchical vocabulary tree modelbased image retrieval approach because of its good scalability and high efficiency. However, there is a large accumulative quantization error in the vocabulary tree (VT) model that may degrade the retrieval precision. To solve this problem, we improve the vector quantization coding in the VT model by introducing a non-negative locality-constrained constraint: the non-negative locality-constrained vocabulary tree-based image retrieval model. The proposed method can effectively improve coding performanee and the discriminative power of local features. Extensive experiments on a large fused finger vein database demonstrate the superiority of our encoding method. Experimental results also show that our retrieval strategy achieves better performanee than other state-of-theart methods, while maintaining low time complexity.

关键词： non-negative locality-constrained vocabulary tree finger vein image retrieval large scale inverted indexing

A hybrid biometric identification framework for high security applications

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Frontiers of Computer Science 2015年第3期9卷 392-401页

作者： Xuzhou LI yilong yin Yanbin NING Gongping YANG Lei PAN School of Computer Science and Technology Shandong University Jinan 250101 China Key Laboratory of Information Security and Intelligent Control of Shandong Province Shandong Youth University of Political Science Jinan 250103 China

Research on biometrics for high security applica- tions has not attracted as much attention as civilian or foren- sic applications. Limited research and deficient analysis so far has led to a lack of general solutions and leaves this as a challenging issue. This work provides a systematic analy- sis and identification of the problems to be solved in order to meet the performance requirements for high security applica- tions, a double low problem. A hybrid ensemble framework is proposed to solve this problem. Setting an adequately high threshold for each matcher can guarantee a zero false accep- tance rate （FAR） and then use the hybrid ensemble framework makes the false reject rate （FRR） as low as possible. Three ex- periments are performed to verify the effectiveness and gener- alization of the framework. First, two fingerprint verification algorithms are fused. In this test only 10.55% of fingerprints are falsely rejected with zero false acceptance rate, this is sig- nificantly lower than other state of the art methods. Second, in face verification, the framework also results in a large re- duction in incorrect classification. Finally, assessing the per- formance of the framework on a combination of face and gait verification using a heterogeneous database show this frame- work can achieve both 0% false rejection and 0% false accep- tance simultaneously.

关键词： biometric verification hybrid ensemble frame-work high security applications

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Sequential quadratic programming enhanced backtracking search algorithm

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Frontiers of Computer Science 2018年第2期12卷 316-330页

作者： Wenting ZHAO Lijin WANG yilong yin Bingqing WANG Yuchun TANG School of Computer Science and Technology Shandong University Jinan 250101 China College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou 350002 China Research Center for Sectional and Imaging Anatomy Shandong University School of Medicine Jinan 250012 China

In this paper, we propose a new hybrid method called SQPBSA which combines backtracking search optimization algorithm （BSA） and sequential quadratic programming （SQP）. BSA, as an exploration search engine, gives a good direction to the global optimal region, while SQP is used as a local search technique to exploit the optimal solution. The experiments are carried on two suits of 28 functions proposed in the CEC-2013 competitions to verify the performance of SQPBSA. The results indicate the proposed method is effective and competitive.

关键词： numerical optimization backtracking search algorithm sequential quadratic programming local search

Recursive narrative alignment for movie narrating

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Science China(Information Sciences) 2020年第7期63卷 230-232页

作者： Zhongyi HAN Hongbo WU Benzheng WEI yilong yin Shuo LI School of Software Shandong University Computational Medicine Lab Shandong University of Traditional Chinese Medicine

Movie narrating can enable the capture of not only the subject matter but also the emotive essence of the subject. For example,"having a good time"or "feeling exhausted", which relates to the narrative of "holding a birthday party". Movie narrating converts the human-like expressivity in movie shots (i.e., sets of film frames) into a cohesive and

关键词： Movie Narrating Visual Captioning Long Short-Term Memory Networks Deep Learning Narrative

Multi-task MIML learning for pre-course student performance prediction

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Frontiers of Computer Science 2020年第5期14卷 113-121页

作者： Yuling Ma Chaoran Cui Jun Yu Jie Guo Gongping Yang yilong yin School of Software Shandong UniversityJinan250100China School of Information Engineering Shandong Yingcai CollegeJinan250104China School of Computer Science and Technology Shandong University of Finance and EconomicsJinan250014China

In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning ***,given the large size of a course’s students at universities,it has become impossible for teachers to keep track of the performance of individual *** this circumstance,an academic early warning system is desirable,which automatically detects students with difficulties in learning(i.e.,at-risk students)prior to a course ***,previous studies are not well suited to this purpose for two reasons:1)they have mainly concentrated on e-learning platforms,e.g.,massive open online courses(MOOCs),and relied on the data about students’online activities,which is hardly accessed in traditional teaching scenarios;and 2)they have only made performance prediction when a course is in progress or even close to the *** this paper,for traditional classroom-teaching scenarios,we investigate the task of pre-course student performance prediction,which refers to detecting at-risk students for each course before its *** better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)***,given the problem of data scarcity,we propose a novel multi-task learning method,i.e.,MIML-Circle,to predict the performance of students from different specialties in a unified *** experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods.

关键词： educational data mining academic early warning system student performance prediction multi-instance multi-label learning multi-task learning