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Supervised dictionary learning for blind image quality assessment using quality-constraint sparse coding

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年 33卷 123-133页

作者： Jiang, Qiuping Shao, Feng Jiang, Gangyi Yu, Mei Peng, Zongju Ningbo Univ Fac Informat Sci & Engn 818 Fenghua Rd Ningbo 315211 Zhejiang Peoples R China

Blind image quality assessment (BIQA) involves predicting the perceptual quality of distorted images without using their corresponding reference images as benchmark. Especially, it is desirable and meaningful to design effective opinion-free BIQA (OF-BIQA) model to predict image quality without depending on human subjective score. Toward this end, we propose a supervised dictionary learning framework for OF-BIQA using quality-constraint sparse coding. The prominent advantage of the proposed model is that "ground truth" quality scores derived from existing full-reference IQA (FR-IQA) metrics are incorporated into the traditional dictionary learning framework so that a quality-aware sparse model can be learnt. Since the goal of BIQA is to predict the quality score, the introduction of quality information into dictionary learning can be regard as a supervised dictionary learning framework. In the detailed implementation, a quality-aware regularization term is added to the traditional dictionary learning formulation, such that a feature-aware dictionary and a quality-aware dictionary can be learned jointly. Especially, these two dictionaries share the same sparse coefficients, so that the reconstruction errors from the image feature vectors and quality score vectors are both minimized. Once the feature-aware and quality-aware dictionaries are jointly learned, given a testing sample, we first abstract its feature vector and then compute the corresponding sparse coefficients w.r.t. the learnt feature-aware dictionary, finally its quality score can be directly reconstructed based on the learnt quality-aware dictionary and the estimated sparse coefficients w.r.t. the learnt feature-aware dictionary. The reconstructed quality score is expected to well approximate to the "ground truth" quality score. Thorough validation experiments on three publicly available IQA benchmark databases demonstrate the promising performance of the proposed OF-BIQA model both on the prediction acc

关键词： Blind image quality assessment (BIQA) Supervised dictionary learning sparse coding sparse representation Quality-constraint Opinion free Gabor Histogram of Oriented Gradient (HOG)

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Statistics on Temporal Changes of sparse coding Coefficients in Spatial Pyramids for Human Action Recognition

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2015年第9期E98D卷 1711-1714页

作者： Li, Yang Ye, Junyong Wang, Tongqing Huang, Shijian Chongqing Univ Minist Educ Key Lab Optoelect Technol & Syst Chongqing 630044 Peoples R China

Traditional sparse representation-based methods for human action recognition usually pool over the entire video to form the final feature representation, neglecting any spatio-temporal information of features. To employ spatio-temporal information, we present a novel histogram representation obtained by statistics on temporal changes of sparse coding coefficients frame by frame in the spatial pyramids constructed from videos. The histograms are further fed into a support vector machine with a spatial pyramid matching kernel for final action classification. We validate our method on two benchmarks, KTH and UCF Sports, and experiment results show the effectiveness of our method in human action recognition.

关键词： sparse coding temporal changes spatial pyramid human action recognition

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Cell recognition based on topological sparse coding for microscopy imaging of focused ultrasound treatment

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BMC MEDICAL IMAGING 2015年第1期15卷 1-8页

作者： Wang, Zhenyou Zhu, Jiang Xue, Yanmei Song, Changxiu Bi, Ning Sun Yat Sen Univ Sch Math & Computat Sci Guangzhou 510275 Guangdong Peoples R China Guangdong Univ Technol Fac Appl Math Guangzhou Guangdong Peoples R China Nanjing Univ Informat Sci Technol Sch Math & Stat Nanjing Jiangsu Peoples R China Zhejiang Univ Coll Med Sir Run Shaw Hosp Dept Ultrasound Hangzhou 310003 Zhejiang Peoples R China

Background: Ultrasound is considered a reliable, widely available, non-invasive, and inexpensive imaging technique for assessing and detecting the development phases of cancer;both in vivo and ex vivo, and for understanding the effects on cell cycle and viability after ultrasound treatment. Methods: Based on the topological continuity characteristics, and that adjacent points or areas represent similar features, we propose a topological penalized convex objective function of sparse coding, to recognize similar cell phases. Results: This method introduces new features using a deep learning method of sparse coding with topological continuity characteristics. Large-scale comparison tests demonstrate that the RAW can outperform SIFT GIST and HoG as the input features with this method, achieving higher sensitivity, specificity, F1 score, and accuracy. Conclusions: Experimental results show that the proposed topological sparse coding technique is valid and effective for extracting new features, and the proposed system was effective for cell recognition of microscopy images of the MDA-MB-231 cell line. This method allows features from sparse coding learning methods to have topological continuity characteristics, and the RAW features are more applicable for the deep learning of the topological sparse coding method than SIFT GIST and HoG.

关键词： Topological continuity characteristics sparse coding Focused ultrasound Microscopy imaging

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Recognizing Objectionable Pictures Using sparse coding 23

Recognizing Objectionable Pictures Using Sparse Coding

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23rd Iranian Conference on Electrical Engineering

作者： Moradi, Reza Yousefzadeh, Rahman Iran Univ Sci & Technol Tehran Iran Shahrood Univ technol Shahrood Iran

ISBN: (纸本)9781479919710

In recent years different methods for detecting objectionable images have proposed. Generally these methods are based on skin color detection and extracting features from human body. In this paper a variant of SPM method is proposed in order to discriminate normal images from objectionable ones. In this method first SIFT features are extracted. Next features are learned by sparse coding the features of previous step. Finally classes are separated by a linear SVM. This approach remarkably improves the scalability of the training phase. The proposed system is tested on 80,000 images and experiments indicate that it outperforms other methods including methods based on histogram features and nonlinear classifiers.

关键词： sparse coding Objectionable image recognition SIFT

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Semi-supervised Discriminant Analysis Based on sparse-coding Theory 35

Semi-supervised Discriminant Analysis Based on Sparse-coding...

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35th Chinese Control Conference (CCC)

作者： Zhang, Qi Chu, Tianguang Peking Univ Coll Engn State Key Lab Turbulence & Complex Syst Beijing 100571 Peoples R China

ISBN: (纸本)9789881563910

We consider the problem of semi-supervised graphbased learning. Since in semi-supervised settings, the labeled information is limited, we first propose la-norm-based label propagation (a-SLP) model to estimate the soft labels by using small set of labeled and large amount of unlabeled training data, and thereby enrich the supervised information. Based on the a-SLP results, we conduct semi-supervised discriminant analysis and present graph-based embedding (SGE) approach by incorporating the estimated soft labels with the local geometric information of both the within-class and between-class training data. Within-class affinity matrices and between-class weight matrix are introduced to preserve the propagated label information and local geometric information of data. This gets rid of the problem that merely concerning about the soft labels may lead to errors. By minimizing the locality-preserved within-class distances and maximizing the weighted betweenclass separability, subspaces that characterize the intrinsic data structure can be well captured. Experiments in face recognition verify the validity and effectiveness of the proposed methods.

关键词： Semi-supervised leaning feature extraction sparse coding face recognition

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sparse-coding-based Household Clustering for Demand Response Services 25

Sparse-Coding-based Household Clustering for Demand Response...

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25th IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ikeda, Shintaro Nishi, Hiroaki Keio Univ Grad Sch Sci & Technol Fujisawa Kanagawa Japan Keio Univ Dept Syst Design Fac Sci & Technol Fujisawa Kanagawa Japan

ISBN: (纸本)9781509008735

For the development of a smart grid, smart meter is a key device to measure the electric power usage of network-connected houses. Smart meters are currently being installed into households, and play the important role for providing a demand response service. A demand response is a necessary service in order to adjust supply-demand balancing because the balance is kept by the cost in the electricity market. Therefore, the customers of the electricity supply companies are expected to be optimally assigned as a group. In this paper, we separate a set of households into clusters as optimally assigned customers. When conducting the clustering, the utilization of unsupervised learning using data from a smart meter is required. In this study, we propose a method of household clustering for a demand response event by sparse coding, which is a type of neural network. The proposed method generates a power consumption model of each household, finds simple relationship distances between households, and conducts hierarchical clustering based on these distances. In addition, to extract the characteristics of fluctuating load usage, we conduct data normalization that cuts off at a fixed load usage of each household. To confirm the effect of the proposed clustering method, the usage tendency of household air conditioning (A/C) units was evaluated.

关键词： Smart meter Household clustering Demand response sparse coding

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Non-Negative sparse coding with Regularizer for Image Classification 15

Non-Negative Sparse Coding with Regularizer for Image Classi...

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IEEE Winter Conference on Applications of Computer Vision (WACV 2015)

作者： Mukherjee, Lopamudra Hall, Alex Univ Wisconsin Whitewater Whitewater WI 53190 USA Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781479966837

sparse coding has been successfully applied to many image and object classification problems in recent years. In such tasks, the problem is commonly formulated as representing an image or signal as a sparse linear combination of dictionary elements, where such dictionary elements capture high level patterns in the data;however, relationships between the dictionary elements are generally not taken into account in such frameworks. In this paper, we address this issue on two levels. First, we incorporate high-level domain specific information within sparse coding. Second, while generating the dictionary itself, we develop a model that encourages incoherence among its components so as to build a compact dictionary. This leads to a powerful framework that improves the performance of sparse coding, by giving it the right kind of "guidance" for identifying objects, particularly in presence of noise and other variations. We demonstrate the efficacy of our model through extensive experiments, which show that our method meets or exceeds the performance of state-of-the-art algorithms in several image and object classification tasks.

关键词： image classification image coding dictionary elements image classification problem nonnegative sparse coding-with-regularizer object classification problem sparse linear combination Dictionaries Encoding Face Optimization sparse matrices Training Vectors Image classification Dictionaries as Topic sparse coding sparse matrix image coding Linear Combination Face Face Functional training

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Discriminative sparse coding of ECG During Ventricular Arrhythmias Using LC-K-SVD Approach 37

Discriminative Sparse Coding of ECG During Ventricular Arrhy...

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37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Kalaji, I. Balasundaram, K. Umapathy, K. Ryerson Univ Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781424492701

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are two major types of ventricular arrhythmias that results due to abnormalities in the electrical activation in the ventricles of the heart. VF is the lethal of the two arrhythmias, which may lead to sudden cardiac death. The treatment options for the two arrhythmias are different. Therefore, detection and characterization of the two arrhythmias is critical to choose appropriate therapy options. Due to the time-varying nature of the signal content during cardiac arrhythmias, modeling and extracting information from them using time and frequency localized functions would be ideal. To this effect, in this work, we perform discriminative sparse coding of the ECG during ventricular arrhythmia with hybrid time-frequency dictionaries using the recently introduced Label consistent K-SVD (LC-K-SVD) approach. Using 944 segments of ventricular arrhythmias extracted from 23 patients in the Malignant Ventricular Ectopy and Creighton University Tachy-Arrhythmia databases, an overall classification accuracy of 71.55% was attained with a hybrid dictionary of Gabor and symlet4 atoms. In comparison, for the same database and non-trained dictionary (i.e the original dictionary) the classification accuracy was found to be 62.71%. In addition, the modeling error using the trained dictionary from LC-K-SVD approach was found to be significantly lower to the one using the non-trained dictionary.

关键词： ECG SVD LF electrochemical grinding Electrocardiography Ventricular Arrhythmia Cardiac Arrhythmias Swine vesicular disease Singular value decomposition Dictionaries as Topic low frequencies sparse coding Heart Ventricles

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Defect Consideratons for Robust sparse coding Using Memristor Arrays

Defect Consideratons for Robust Sparse Coding Using Memristo...

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IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH)

作者： Sheridan, Patrick Lu, Wei D. Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA

The performance of a memristor network with defects is investigated on a sparse coding task. It was found that moderate levels SA0 faults do not significantly impact algorithm performance, while SA1 faults effectively... 详细信息

ISBN: (纸本)9781467378499

关键词： Memristor fault tolerance sparse coding crossbar

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VISUAL TRACKING VIA ORTHOGONAL sparse coding

VISUAL TRACKING VIA ORTHOGONAL SPARSE CODING

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IEEE International Conference on Image Processing (ICIP)

作者： Wang, Jing Wang, Yiyang Liu, Risheng Su, Zhixun Dalian Univ Technol Sch Math Sci Dalian Peoples R China Dalian Univ Technol Sch Software Technol Dalian Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Jiangsu Peoples R China

ISBN: (纸本)9781479983391

In this paper, we incorporate sparse coding and orthogonal dictionary learning into a unified framework, named orthogonal sparse coding (OSC), for robust visual tracking. Different from previous tracking methods, which often use redundant dictionaries, OSC enforces an orthogonality constraint in the dictionary learning step to adaptively capture the structures of the video sequences. Moreover, a l(0) norm regularizer is introduced in OSC formulation to address the severe noise problems, illumination changes, and occlusions in real world videos. As a nontrivial byproduct, we develop an efficient numerical solver to address the optimization issues of our OSC model. Experimental results on various challenging video sequences show that the proposed method achieves better performance both on accuracy and speed compared to proposed state-of-the-art methods.

关键词： Visual tracking particle filter orthogonal dictionary sparse coding l(0) regularization

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