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Exploiting label consistency in structured sparse representation for classification

NEURAL COMPUTING & APPLICATIONS 2019年第10期31卷 6509-6520页

作者： Huang, Yan Quan, Yuhui Liu, Tao Xu, Yong South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China

sparse representation with adaptive dictionaries has emerged as a promising tool in computer vision and pattern analysis. While standard sparsity promoted by l(0) or l(1) regularization has been widely used, recent approaches seek for kinds of structured sparsity to improve the discriminability of sparse codes. For classification, label consistency is one useful concept regarding structured sparsity, which relates class labels to dictionary atoms for generating discriminative sparsity patterns. Motivated by the limitations of existing label-consistent regularization methods, in this paper, we investigate the exploitation of label consistency and propose an effective sparse coding approach. The proposed approach enforces the sparse approximation of a label consistency matrix by sparse code during dictionary learning, which encourages the supports of sparse codes to be consistent for intra-class signals and distinct for inter-class signals. Thus, the learned dictionary can induce discriminative sparsity patterns when used in sparse coding. Moreover, the proposed method is computationally efficient, as the label consistency regularization developed in our method brings very little additional computational cost in solving the related sparse coding problem. The effectiveness of the proposed method is demonstrated with several recognition tasks, and the experimental results show that our method is very competitive with some state-ofthe-art approaches.

关键词： sparse coding Label consistency Structured sparsity Image classification

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sparse representation for image classification via paired dictionary learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第12期78卷 16945-16963页

作者： Wang, Hui-Hung Tu, Chia-Wei Chiang, Chen-Kuo Natl Chung Cheng Univ CIRAS Adv Inst Mfg High tech Innovat 168Sec 1Univ Rd Chiayi Taiwan Natl Chung Cheng Univ Dept Comp Sci & Informat Engn 168Sec 1Univ Rd Chiayi Taiwan

sparse coding technique is usually applied for feature representation. To learn discriminative features for visual recognition, a dictionary learning method, called Paired Discriminative K-SVD (PD-KSVD), is presented in this paper. Firstly, to reduce the reconstruction error of positive class while increasing the errors of negative classes, the scheme inverted signal is applied to the negative training samples. Then, the class-specific sub-dictionaries are learned from pairs of positive and negative classes to jointly achieve high discrimination and low reconstruction errors for sparse coding. Multiple sub-dictionaries are concatenated with respect to the same negative class so that the non-zero sparse coefficients can be discriminatively distributed to improve classification accuracy. Last, sparse coefficients are solved via the concatenated sub-dictionaries and used to train the classifier. Compared to the existing dictionary learning methods, PD-KSVD method achieves superior performance in a variety of visual recognition tasks on several publicly available datasets.

关键词： Dictionary learning sparse coding Visual recognition

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Single sample face recognition via BoF using multistage KNN collaborative coding

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第10期78卷 13297-13311页

作者： Liu, Fan Yang, Sai Ding, Yuhua Xu, Feng Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China Hohai Univ Nantong Ocean & Coastal Engn Res Inst Nantong Peoples R China Nantong Univ Sch Elect Engn Nantong Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

In this paper, we propose a multistage KNN collaborative coding based Bag-of-Feature (MKCC-BoF) method to address SSPP problem, which tries to weaken the semantic gap between facial features and facial identification. First, local descriptors are extracted from the single training face images and a visual dictionary is obtained offline by clustering a large set of descriptors with K-means. Then, we design a multistage KNN collaborative coding scheme to project local features into the semantic space, which is much more efficient than the most commonly used non-negative sparse coding algorithm in face recognition. To describe the spatial information as well as reduce the feature dimension, the encoded features are then pooled on spatial pyramid cells by max-pooling, which generates a histogram of visual words to represent a face image. Finally, a SVM classifier based on linear kernel is trained with the concatenated features from pooling results. Experimental results on three public face databases show that the proposed MKCC-BoF is much superior to those specially designed methods for SSPP problem. Moreover, it also has great robustness to expression, illumination, occlusion and, time variation.

关键词： Bag-of-feature Semantic gap Single sample per person sparse coding

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Saliency Detection Using Global and Local Information Under Multilayer Cellular Automata

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IEEE ACCESS 2019年 7卷 72736-72748页

作者： Liu, Yihang Yuan, Peiyan Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Henan Normal Univ Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China

To detect the salient object in natural images with low contrast and complex backgrounds, a saliency detection method that fuses global and local information under multilayer cellular automata is proposed. First, a global saliency map was obtained by the iteratively trained convolutional neural network (CNN)-based encoder-decoder model. Moreover, to transmit high-level information to the lower-level layers and further reinforce the object edge, the skip connections and edge penalty term were added to the network. Second, the foreground and background codebooks were generated by the global saliency map, and sparse coding was subsequently obtained by the locality-constrained linear coding model. Thus, a local saliency map was generated. Finally, the final saliency map was obtained by fusing the global and local saliency maps under the multilayer cellular automata framework. The experimental results show that the average F-measure of our method on the MSRA 10K, ECSSD, DUT-OMRON, HKU-IS, THUR 15K, and XPIE datasets is 93.4%, 89.5%, 79.4%, 88.7%, 73.6%, and 85.2%, respectively, and the MAE is 0.046, 0.067, 0.054, 0.044, 0.072, and 0.049. Ultimately, these findings prove that our method has both high saliency detection accuracies and strong generalization abilities. In particular, our method can effectively detect the salient object of natural images with low contrast and complex backgrounds.

关键词： Saliency detection global and local maps multilayer cellular automata CNN-based encoder-decoder model sparse coding

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sparse Recovery and Dictionary Learning From Nonlinear Compressive Measurements

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第21期67卷 5659-5670页

作者： Rencker, Lucas Bach, Francis Wang, Wenwu Plumbley, Mark D. Univ Surrey Ctr Vis Speech & Signal Proc Guildford GIJ2 7XH Surrey England INRIA Sierra Project Team F-75012 Paris France

sparse coding and dictionary learning are popular techniques for linear inverse problems such as denoising or inpainting. However in many cases, the measurement process is nonlinear, for example for clipped, quantized or 1-bit measurements. These problems have often been addressed by solving constrained sparse coding problems, which can be difficult to solve, and assuming that the sparsifying dictionary is known and fixed. Here we propose a simple and unified framework to deal with nonlinear measurements. We propose a cost function that minimizes the distance to a convex feasibility set, which models our knowledge about the nonlinear measurement. This provides an unconstrained, convex, and differentiable cost function that is simple to optimize, and generalizes the linear least squares cost commonly used in sparse coding. We then propose proximal based sparse coding and dictionary learning algorithms, that are able to learn directly from nonlinearly corrupted signals. We show how the proposed framework and algorithms can be applied to clipped, quantized and 1-bit data.

关键词： Quantization (signal) Encoding Machine learning Dictionaries Cost function Signal processing algorithms Distortion measurement sparse coding dictionary learning nonlinear measurements saturation quantization 1-bit sensing

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Discrete Robust Supervised Hashing for Cross-Modal Retrieval

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IEEE ACCESS 2019年 7卷 39806-39814页

作者： Yao, Tao Zhang, Zhiwang Yan, Lianshan Yue, Jun Tian, Qi Ludong Univ Dept Informat & Elect Engn Yantai 264025 Peoples R China Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligent Se Nanjing 210094 Jiangsu Peoples R China Southwest Jiaotong Univ Yantai Res Inst New Generat Informat Technol Chengdu 264000 Sichuan Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

Cross-modal hashing has been studied extensively in the past decades for its significant advantage in computation and storage cost. For heterogeneous data points, the cross-modal hashing aims at learning a sharing Hamming space in where one query from one modality can retrieve relevant items of another modality. Although the cross-modal hashing method has achieved significant progress, there are some limitations that need to be further solved. First, to leverage the semantic information in hash codes, most of them learn hash codes from a similarity matrix, which is constructed by class labels directly, ignoring the fact that the class labels may contain noises in the real world. Second, most of them relax the discrete constraint on hash codes, which may cause large quantization error and inevitably results in suboptimal performance. To address the above issues, we propose a discrete robust supervised hashing (DRSH) algorithm in this paper. Specifically, both the class labels and features from different modalities are first fused to learn a robust similarity matrix through low-rank constraint that can reveal its structure and capture the noises in it. And then, hash codes are generated by preserving the robust similarity matrix-based similarities in the sharing Hamming space. The optimization is challenging due to the discrete constraint on hash codes. Finally, a discrete optimal algorithm is proposed to address this issue. We evaluate the DRSH on three real-world datasets, and the results demonstrate the superiority of DRSH over several existing hashing methods.

关键词： Cross-modal retrieval roust hashing sparse coding low-rank

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sparse coding-BASED TOPIC MODEL FOR REMOTE SENSING IMAGE SEGMENTATION

SPARSE CODING-BASED TOPIC MODEL FOR REMOTE SENSING IMAGE SEG...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Jun Shi Zhiguo Jiang Hao Feng Yibing Ma Image Processing Center School of Astronautics Beijing University of Aeronautics and Astronautics

ISBN: (纸本)9781479911127

Land cover segmentation can be viewed as topic assignment that the pixels are grouped into homogeneous regions according to different semantic topics in topic model. In this paper, we propose a novel topic model based on sparse coding for segmenting different kinds of land covers. Different from conventional topic models which generally assume each local feature descriptor is related to only one visual word of the codebook, our method utilizes sparse coding to characterize the potential correlation between the descriptor and multiple words. Therefore each descriptor can be represented by a small set of words. Furthermore, in this paper probabilistic Latent Semantic Analysis (pLSA) is applied to learn the latent relation among word, topic and document due to its simplicity and low computational cost. Experimental results on remote sensing image segmentation demonstrate the excellent superiority of our method over k-means clustering and conventional pLSA model.

关键词： Remote sensing sparse coding PLSA Land cover segmentation

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High Fidelity Single-Pixel Imaging

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2019年第2期11卷 1-9页

作者： Deng, Chao Hu, Xuemei Li, Xiaoxu Suo, Jinli Zhang, Zhili Dai, Qionghai Tsinghua Univ Dept Automat Beijing 100084 Peoples R China High Tech Inst Xian Xian 710025 Shaanxi Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210000 Jiangsu Peoples R China

Single-pixel imaging (SPI) is an emerging technique which has attracted wide attention in various fields. However, restricted by the low reconstruction quality and large amount of requisite measurements, SPI's practical application is still in its infancy. Inspired by the fact that natural scenes exhibit unique degenerated structures in the low-dimensional subspace, we propose to take advantage of such local prior via convolutional sparse coding to implement high fidelity SPI. Specifically, we can represent the target scene via convolving with a set of statistically learned kernels, with the convolution coefficient matrix being sparse. We introduce the above local prior into conventional SPI framework to promote the final reconstruction quality. Experiments both on synthetic data and real captured data demonstrate that our method can achieve better reconstruction from the same measurements, or reduce the number of required measurements for the same reconstruction quality.

关键词： Single-pixel imaging high fidelity sparse coding local prior

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A Survey of the Methods on Fingerprint Orientation Field Estimation

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IEEE ACCESS 2019年 7卷 32644-32663页

作者： Bian, Weixin Xu, Deqin Li, Qingde Cheng, Yongqiang Jie, Biao Ding, Xintao Anhui Normal Univ Sch Comp & Informat Wuhu 241002 Peoples R China Anhui Prov Key Lab Network & Informat Secur Wuhu 241002 Peoples R China Univ Hull Dept Comp Sci & Technol Kingston Upon Hull HU6 7RX N Humberside England

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods.

关键词： Fingerprint identification fingerprint orientation field estimation sparse coding dictionary learning convolutional neural networks

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LOSSY COMPRESSIVE SENSING BASED ON ONLINE DICTIONARY LEARNING

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COMPUTING AND INFORMATICS 2019年第1期38卷 151-172页

作者： Ulku, Irem Kizgut, Ersin Cankaya Univ Dept Elect & Elect Engn Eskisehir Yolu 29 Km TR-06790 Ankara Turkey Univ Politecn Valencia IUMPA E-46071 Valencia Spain

In this paper, a lossy compression of hyperspectral images is realized by using a novel online dictionary learning method in which three dimensional datasets can be compressed. This online dictionary learning method and blind compressive sensing (BCS) algorithm are combined in a hybrid lossy compression framework for the first time in the literature. According to the experimental results, BCS algorithm has the best compression performance when the compression bit rate is higher than or equal to 0.5 bps. Apart from observing rate-distortion performance, anomaly detection performance is also tested on the reconstructed images to measure the information preservation performance.

关键词： Hyperspectral imaging compression algorithms dictionary learning sparse coding

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