Image de-noising based on learned dictionary

2011 ◽  
Author(s):  
Chaozhu Zhang ◽  
Liang Zhao
Keyword(s):  
Learned Dictionary

scholarly journals Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Dan Ma ◽  
Yixiang Lu ◽  
Yushun Zhang ◽  
Hua Bao ◽  
Xueming Peng
Keyword(s):  
Learning Strategy ◽  
Vibration Signal ◽  
Collaborative Representation ◽  
Rolling Bearings ◽  
Frequency Spectra ◽  
Similar Frequency ◽  
Vibration Signals ◽  
Redundant Dictionary ◽  
Learned Dictionary

In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any test sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs.

Compact dictionary pair learning and refining based on principal components analysis

2019 ◽  
Vol 17 (05) ◽  
pp. 1950033
Author(s):  
Yibin Yu ◽  
Min Yang ◽  
Yulan Zhang ◽  
Shifang Yuan
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
State Of The Art ◽  
Great Success ◽  
Computation Complexity ◽  
Training Stage ◽  
Learned Dictionary ◽  
Sample Data ◽  
Components Analysis ◽  
Row Space

Although traditional dictionary learning (DL) methods have made great success in pattern recognition and machine learning, it is extremely time-consuming, especially in the training stage. The projective dictionary pair learning (DPL) learned the synthesis dictionary and the analysis dictionary jointly to achieve a fast and accurate classifier. However, the dictionary pair is initialized as random matrices without using any data samples information, it required many iterations to ensure convergence. In this paper, we propose a novel compact DPL and refining method based on the observation that the eigenvalue curve of sample data covariance matrix usually decrease very fast, which means we can compact the synthesis dictionary and analysis dictionary. For each class of the data samples, we utilize the principal components analysis (PCA) to retain global important information and compact the row space of a synthesis dictionary and the column space of an analysis dictionary in the first stage. We further refine the learned dictionary pair to achieve a more accurate classifier during compact dictionary pair refining, which combines the orthogonality of PCA with the redundancy of DL. We solve this refining problem in closed-form completely, naturally reducing the computation complexity significantly. Experimental results on the Extended YaleB database and AR database show that the proposed method achieves competitive accuracy and low computational complexity compared with other state-of-the-art methods.

Top-Down Saliency Detection via Hidden Semantic Information

2014 ◽  
Vol 678 ◽  
pp. 116-119
Author(s):  
Ling Feng Yuan ◽  
Yu Liang Du ◽  
Wei Bing Wan
Keyword(s):  
Semantic Information ◽  
Viterbi Algorithm ◽  
Hidden Markov ◽  
Saliency Detection ◽  
Saliency Map ◽  
Histogram Of Oriented Gradient ◽  
Search Mechanism ◽  
Learned Dictionary ◽  
Salient Regions ◽  
Spatial Pyramid

Saliency detection has been applied in many cases. This paper proposes a 2D hidden Markov model (2D-HMM) which exploits the hidden semantic information of image to detect the salient regions. A spatial pyramid Histogram of Oriented Gradient (SP-HOG) descriptor is used to extract feature. After encoding the image by a learned dictionary, the 2D-viterbi algorithm is applied to inferring the saliency map. This model can depict the shapes of targets, and also it is robust to the targets’ change of posture and viewpoint. To validate the model with human’s visual search mechanism, eye track experiment is employed to train our model directly from the eye data. The results show that our model achieves a better performance than eye data. Moreover, it indicates that learning from eye track data to figure out their targets is possible.

Sparse representation over learned dictionary for symbol recognition

2016 ◽  
Vol 125 ◽  
pp. 36-47 ◽  
Author(s):  
Thanh Ha Do ◽  
Salvatore Tabbone ◽  
Oriol Ramos Terrades
Keyword(s):  
Sparse Representation ◽  
Symbol Recognition ◽  
Learned Dictionary

scholarly journals A method of combining coherence-constrained sparse coding and dictionary learning for denoising

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. V137-V148 ◽  
Author(s):  
Pierre Turquais ◽  
Endrias G. Asgedom ◽  
Walter Söllner
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Random Noise ◽  
A Priori ◽  
Seismic Signal ◽  
Noise Variance ◽  
A Priori Information ◽  
Seismic Section ◽  
Learned Dictionary ◽  
Priori Information

We have addressed the seismic data denoising problem, in which the noise is random and has an unknown spatiotemporally varying variance. In seismic data processing, random noise is often attenuated using transform-based methods. The success of these methods in denoising depends on the ability of the transform to efficiently describe the signal features in the data. Fixed transforms (e.g., wavelets, curvelets) do not adapt to the data and might fail to efficiently describe complex morphologies in the seismic data. Alternatively, dictionary learning methods adapt to the local morphology of the data and provide state-of-the-art denoising results. However, conventional denoising by dictionary learning requires a priori information on the noise variance, and it encounters difficulties when applied for denoising seismic data in which the noise variance is varying in space or time. We have developed a coherence-constrained dictionary learning (CDL) method for denoising that does not require any a priori information related to the signal or noise. To denoise a given window of a seismic section using CDL, overlapping small 2D patches are extracted and a dictionary of patch-sized signals is trained to learn the elementary features embedded in the seismic signal. For each patch, using the learned dictionary, a sparse optimization problem is solved, and a sparse approximation of the patch is computed to attenuate the random noise. Unlike conventional dictionary learning, the sparsity of the approximation is constrained based on coherence such that it does not need a priori noise variance or signal sparsity information and is still optimal to filter out Gaussian random noise. The denoising performance of the CDL method is validated using synthetic and field data examples, and it is compared with the K-SVD and FX-Decon denoising. We found that CDL gives better denoising results than K-SVD and FX-Decon for removing noise when the variance varies in space or time.

scholarly journals Bearing Fault Diagnosis Based on Improved Locality-Constrained Linear Coding and Adaptive PSO-Optimized SVM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Haodong Yuan ◽  
Jin Chen ◽  
Guangming Dong
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Fixed Number ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Learned Dictionary ◽  
Adaptive Pso ◽  
Diagnosis Method ◽  
Linear Svm

A novel bearing fault diagnosis method based on improved locality-constrained linear coding (LLC) and adaptive PSO-optimized support vector machine (SVM) is proposed. In traditional LLC, each feature is encoded by using a fixed number of bases without considering the distribution of the features and the weight of the bases. To address these problems, an improved LLC algorithm based on adaptive and weighted bases is proposed. Firstly, preliminary features are obtained by wavelet packet node energy. Then, dictionary learning with class-wise K-SVD algorithm is implemented. Subsequently, based on the learned dictionary the LLC codes can be solved using the improved LLC algorithm. Finally, SVM optimized by adaptive particle swarm optimization (PSO) is utilized to classify the discriminative LLC codes and thus bearing fault diagnosis is realized. In the dictionary leaning stage, other methods such as selecting the samples themselves as dictionary and K-means are also conducted for comparison. The experiment results show that the LLC codes can effectively extract the bearing fault characteristics and the improved LLC outperforms traditional LLC. The dictionary learned by class-wise K-SVD achieves the best performance. Additionally, adaptive PSO-optimized SVM can greatly enhance the classification accuracy comparing with SVM using default parameters and linear SVM.

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