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Fast Face Recognition Via sparse coding and Extreme Learning Machine

COGNITIVE COMPUTATION 2014年第2期6卷 264-277页

作者： He, Bo Xu, Dongxun Nian, Rui van Heeswijk, Mark Yu, Qi Miche, Yoan Lendasse, Amaury Ocean Univ China Sch Informat Sci & Engn Qingdao 266100 Shandong Peoples R China Aalto Univ Dept Informat & Comp Sci Aalto 00076 Finland Basque Fdn Sci IKERBASQUE Bilbao 48011 Spain Univ Basque Country Fac Comp Sci Computat Intelligence Grp Donostia San Sebastian Spain

Most face recognition approaches developed so far regard the sparse coding as one of the essential means, while the sparse coding models have been hampered by the extremely expensive computational cost in the implementation. In this paper, a novel scheme for the fast face recognition is presented via extreme learning machine (ELM) and sparse coding. The common feature hypothesis is first introduced to extract the basis function from the local universal images, and then the single hidden layer feedforward network (SLFN) is established to simulate the sparse coding process for the face images by ELM algorithm. Some developments have been done to maintain the efficient inherent information embedding in the ELM learning. The resulting local sparse coding coefficient will then be grouped into the global representation and further fed into the ELM ensemble which is composed of a number of SLFNs for face recognition. The simulation results have shown the good performance in the proposed approach that could be comparable to the state-of-the-art techniques at a much higher speed.

关键词： Extreme learning machine Common feature hypothesis sparse coding Face recognition

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Tactile Sequence Classification using Joint Kernel sparse coding

Tactile Sequence Classification using Joint Kernel Sparse Co...

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International Joint Conference on Neural Networks

作者： Jingwei Yang Huaping Liu Fuchun Sun Meng Gao Department of Electrical and Electronic Engineering Shijiazhuang Tiedao University Shijiazhuang China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781479919611

Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for intelligent robot. However, existing work neglects the intrinsic relation between different fingers which simultaneously contact the object. In this paper, a joint kernel sparse coding model is developed to tackle the multi-finger tactile sequence classification problem. In this model, the intrinsic relations between fingers are explicitly considered using the joint sparse coding which encourages different modal coding to share the same support. The experimental results show that the joint sparse coding achieves better performance than conventional sparse coding.

关键词： joint sparse coding kernel sparse coding tactile sequence classification sparse coding tactile sensors Fingers Joints Joints Touch Seeds friction

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Image Scene Classification Based on Fisher Discriminative Analysis and sparse coding

Image Scene Classification Based on Fisher Discriminative An...

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2015 3rd International Conference on Machinery,Materials and Information Technology Applications(ICMMITA 2015)

作者： Jianliang Meng Rui Ni Ye Wang Peng Zhao North China Electric Power University State Grid Hebei Electric Power Company

Bag of visual words（Bo VW） is currently widely utilized as an image representation model. However, conventional Bo VW methods usually bring drawbacks such as large representation errors, lack of spatial information and weak discrimination. In order to overcome those, this paper proposes a new image scene classification algorithm based on fisher discriminative analysis and sparse coding. Firstly, we construct the non-negative sparse locally linear coding to encode the local features with neighbors’ visual vocabularies, thus to make effective use of images’ spatial information. Secondly, we add fisher discriminative analysis to construct a non-negative sparse locally linear coding model based on fisher discriminative criterion constraint, thus to obtain the images’ discriminative sparse representation, and promote the spatial separability of sparse coefficients and enforce the classification capability of images’ sparse representation. Finally, we combine SVM classifier to perform scene classification. Experiment results show that our algorithm efficiently utilizes spatial information of images and incline to seek images’ discrimination representations, and improves the classification performance, thus it’s more suitable for image classification.

关键词： Scene classification image representation sparse coding

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Efficient Kernel sparse coding via First-Order Smooth Optimization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2014年第8期25卷 1447-1459页

作者： Kim, Minyoung Seoul Natl Univ Sci & Technol Dept Elect & IT Media Engn Seoul 139743 South Korea

We consider the problem of dictionary learning and sparse coding, where the task is to find a concise set of basis vectors that accurately represent the observation data with only small numbers of active bases. Typically formulated as an L1-regularized least-squares problem, the problem incurs computational difficulty originating from the nondifferentiable objective. Recent approaches to sparse coding thus have mainly focused on acceleration of the learning algorithm. In this paper, we propose an even more efficient and scalable sparse coding algorithm based on the first-order smooth optimization technique. The algorithm finds the theoretically guaranteed optimal sparse codes of the epsilon-approximate problem in a series of optimization subproblems, where each subproblem admits analytic solution, hence very fast and scalable with large-scale data. We further extend it to nonlinear sparse coding using kernel trick by showing that the representer theorem holds for the kernel sparse coding problem. This allows us to apply dual optimization, which essentially results in the same linear sparse coding problem in dual variables, highly beneficial compared with the existing methods that suffer from local minima and restricted forms of kernel function. The efficiency of our algorithms is demonstrated for natural stimuli data sets and several image classification problems.

关键词： Dictionary learning first-order smooth optimization kernel representer theorem sparse coding

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Fast Super-Resolution via Patchwise sparse coding

Fast Super-Resolution via Patchwise Sparse Coding

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12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Ni Hao Liu Fanghua Ruan Ruolin Hubei Univ Sci & Technol Sch Elect & Informat Engn Xianning Peoples R China Hubei Univ Sci & Technol Sch Biomed Engn Xianning Peoples R China

ISBN: (纸本)9781467376822

Single image super-resolution via sparse coding is one of the example-based super-resolution methods. The results are promising. But the relatively low speed hinders its real-time application because of the large and complex computation. We propose a Patchwise sparse coding Super-Resolution algorithm to reduce the computation by processing the large mount of patches with patch dimensionality reduction and classification. In our method, the input low-resolution patches are classified and marked by their mean and variance pairs. We use bicubic interpolation, neighbor embedding and sparse coding to different classifications of patches to accelerate the SR process. Additionally we also employ Principle Component Analysis (PCA) dimension reduction to reduce the computation of each patch in the training phase. Finally, the results show us that the computation quantity of the sparse coding image SR is reduced efficiently. The SR time is 17.2s faster than the state-of-the-art SCSR algorithm.

关键词： super-resolution sparse coding patch classification principle component analysis

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sparse coding on Multiple Manifold Data 21

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21st International Conference on Neural Information Processing (ICONIP)

作者： Zhang, Hanchao Xu, Jinhua East China Normal Univ Dept Comp Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9783319126401

sparse coding has been widely used in computer vision. While capturing high-level semantics, the independent coding process neglects connections between data points. Some recent methods use Laplacian matrix to learn sparse representations with locality preserving on the manifold. Considering data points may lie in or close to multiple low dimensional manifolds embedded in the high dimensional descriptor space, we use sparse representations to code the local similarity between data points on each manifold and embed this topology to sparse coding algorithm. By keeping the locality of manifolds we can preserve the similarity and separability at the same time. Experimental results on several benchmark data sets show our algorithm is effective.

关键词： sparse coding sparse representation manifold learning Laplacian matrix locality preserving

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On robust face recognition via sparse coding: the good, the bad and the ugly

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IET BIOMETRICS 2014年第4期3卷 176-189页

作者： Wong, Yongkang Harandi, Mehrtash T. Sanderson, Conrad Natl Univ Singapore SeSaMe Ctr Singapore 117548 Singapore Univ Queensland Sch ITEE Brisbane Qld 4072 Australia NICTA Canberra ACT 2601 Australia Australian Natl Univ Canberra ACT 0200 Australia Queensland Univ Technol Brisbane Qld 4000 Australia

In the field of face recognition, sparse representation (SR) has received considerable attention during the past few years, with a focus on holistic descriptors in closed-set identification applications. The underlying assumption in such SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such an assumption is easily violated in the face verification scenario, where the task is to determine if two faces (where one or both have not been seen before) belong to the same person. In this study, the authors propose an alternative approach to SR-based face verification, where SR encoding is performed on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which then form an overall face descriptor. Owing to the deliberate loss of spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment and various image deformations. Within the proposed framework, they evaluate several SR encoding techniques: l(1)-minimisation, sparse Autoencoder Neural Network (SANN) and an implicit probabilistic technique based on Gaussian mixture models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, on both the traditional closed-set identification task and the more applicable face verification task. The experiments also show that l(1)-minimisation-based encoding has a considerably higher computational cost when compared with SANN-based and probabilistic encoding, but leads to higher recognition rates.

关键词： face recognition Gaussian processes image coding minimisation neural nets visual databases local SR approach AR datasets FERET datasets exYaleB datasets BANCA datasets ChokePoint datasets Gaussian mixture models implicit probabilistic technique SANN sparse autoencoder neural network l1-minimisation image deformations spatial relations multiple region descriptors sparse signals local image patches SR encoding face verification scenario closed-set identification applications holistic descriptors sparse representation sparse coding robust face recognition

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A sparse coding approach for local-to-global 3D shape description

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VISUAL COMPUTER 2014年第11期30卷 1233-1245页

作者： Boscaini, Davide Castellani, Umberto Univ Lugano Lugano Switzerland Univ Verona Dept Comp Sci I-37100 Verona Italy LASMEA Lab Clermont Ferrand France

The definition of reliable shape descriptors is an essential topic for 3D object retrieval. In general, two main approaches are considered: global, and local. Global approaches are effective in describing the whole object, while local ones are more suitable to characterize small parts of the shape. Recently some strategies to combine these two approaches have been proposed which are mainly concentrated to the so-called bag of words paradigm. With this paper we address this problem and propose an alternative strategy that goes beyond the bag of word approach. In particular, a sparse coding technique is exploited for the 3D domain: a set of local shape descriptors are collected from the shape, and then a dictionary is trained as generative model. In this fashion the dictionary is used as global shape descriptor for shape retrieval purposes. Several experiments are performed on standard databases in order to evaluate the proposed method in challenging situations like the case of 'SHREC 2011: robustness benchmark' where strong shape transformations are included, and the case of 'SHREC 2007: partial matching track' where composite models are considered in the query phase. A drastic improvement of the proposed method is observed by showing that sparse coding approach is particularly suitable for local-to-global description and outperforms other approaches such as the bag of words.

关键词： 3D object retrieval sparse coding Bag of words Partial shape matching

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Efficient Hardware Architecture for sparse coding

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2014年第16期62卷 4173-4186页

作者： Kim, Jung Kuk Knag, Phil Chen, Thomas Zhang, Zhengya Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

sparse coding encodes natural stimuli using a small number of basis functions known as receptive fields. In this work, we design custom hardware architectures for efficient and high-performance implementations of a sparse coding algorithm called the sparse and independent local network (SAILnet). A study of the neuron spiking dynamics uncovers important design considerations involving the neural network size, target firing rate, and neuron update step size. Optimal tuning of these parameters keeps the neuron spikes sparse and random to achieve the best image fidelity. We investigate practical hardware architectures for SAILnet: a bus architecture that provides efficient neuron communications, but results in spike collisions;and a ring architecture that is more scalable, but causes neuron misfires. We show that the spike collision rate is reduced with a sparse spiking neural network, so an arbitration-free bus architecture can be designed to tolerate collisions without the need of arbitration. To reduce neuron misfires, we design a latent ring architecture to damp the neuron responses for an improved image fidelity. The bus and the ring architecture can be combined in a hybrid architecture to achieve both high throughput and scalability. The three architectures are synthesized and place-and-routed in a 65 nm CMOS technology. The proof-of-concept designs demonstrate a high sparse coding throughput up to 952 M pixels per second at an energy consumption of 0.486 nJ per pixel.

关键词： Algorithm and architecture co-optimization hardware acceleration neural network architecture sparse and independent local network sparse coding

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Speech enhancement with a GSC-like structure employing sparse coding

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第12期15卷 1154-1163页

作者： Li-chun YANG Yun-tao QIAN College of Computer Science and Technology Zhejiang University Intelligent Control Research Institute Zhejiang Wanli University

Speech communication is often influenced by various types of interfering signals. To improve the quality of the desired signal, a generalized sidelobe canceller(GSC), which uses a reference signal to estimate the interfering signal, is attracting attention of researchers. However, the interference suppression of GSC is limited since a little residual desired signal leaks into the reference signal. To overcome this problem, we use sparse coding to suppress the residual desired signal while preserving the reference signal. sparse coding with the learned dictionary is usually used to reconstruct the desired signal. As the training samples of a desired signal for dictionary learning are not observable in the real environment, the reconstructed desired signal may contain a lot of residual interfering signal. In contrast,the training samples of the interfering signal during the absence of the desired signal for interferer dictionary learning can be achieved through voice activity detection(VAD). Since the reference signal of an interfering signal is coherent to the interferer dictionary, it can be well restructured by sparse coding, while the residual desired signal will be removed. The performance of GSC will be improved since the estimate of the interfering signal with the proposed reference signal is more accurate than ever. Simulation and experiments on a real acoustic environment show that our proposed method is effective in suppressing interfering signals.

关键词： Generalized sidelobe canceller Speech enhancement Voice activity detection Dictionary learning sparse coding

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