Shape sequence deformation model based structured matrix decomposition

2017 ◽  
Author(s):  
Fuyun He ◽  
Zhisheng Zhang ◽  
Qi Zhang ◽  
Rong Zhou
Keyword(s):  
Matrix Decomposition ◽  
Deformation Model ◽  
Model Based ◽  
Structured Matrix

scholarly journals Performance Analysis of Structured Matrix Decomposition with Contour Based Spatial Prior for Extracting Salient Object from Complex Scene

2019 ◽  
Vol 15 (2) ◽  
pp. 133-140
Author(s):  
Ramesh Bhandari ◽  
Sharad Kumar Ghimire
Keyword(s):  
Computer Vision ◽  
Research Work ◽  
Similarity Index ◽  
Matrix Decomposition ◽  
Low Rank ◽  
Salient Object ◽  
Complex Scene ◽  
Decomposition Model ◽  
Structured Matrix ◽  
Segmentation Image

 Automatically extracting most conspicuous object from an image is useful and important for many computer vision related tasks. Performance of several applications such as object segmentation, image classification based on salient object and content based image editing in computer vision can be improved using this technique. In this research work, performance of structured matrix decomposition with contour based spatial prior is analyzed for extracting salient object from the complex scene. To separate background and salient object, structured matrix decomposition model based on low rank matrix recovery theory is used along with two structural regularizations. Tree structured sparsity inducing regularization is used to capture image structure and to enforce the same object to assign similar saliency values. And, Laplacian regularization is used to enlarge the gap between background part and salient object part. In addition to structured matrix decomposition model, general high level priors along with biologically inspired contour based spatial prior is integrated to improve the performance of saliency related tasks. The performance of the proposed method is evaluated on two demanding datasets, namely, ICOSEG and PASCAL-S for complex scene images. For PASCAL-S dataset precision recall curve of proposed method starts from 0.81 and follows top and right-hand border more than structured matrix decomposition which starts from 0.79. Similarly, structural similarity index score, which is 0.596654 and 0.394864 without using contour based spatial prior and 0.720875 and 0.568001 using contour based spatial prior for ICOSEG and PASCAL-S datasets shows improved result.

Super-resolution reconstruction and higher-degree function deformation model based matching for Chang’E-1 lunar images

2009 ◽  
Vol 52 (12) ◽  
pp. 3468-3476 ◽  
Author(s):  
LiChun Li ◽  
QiFeng Yu ◽  
Yun Yuan ◽  
Yang Shang ◽  
HongWei Lu ◽  
...  
Keyword(s):  
Super Resolution ◽  
Deformation Model ◽  
Model Based ◽  
Degree Function

A Blood Vessel Deformation Model Based Virtual-reality Simulator for the Robotic Catheter Operating System

2015 ◽  
Vol 2 (3) ◽  
pp. 126-131 ◽  
Author(s):  
Yu Wang ◽  
Shuxiang Guo ◽  
Takashi Tamiya ◽  
Hideyuki Hirata ◽  
Hidenori Ishihara
Keyword(s):  
Operating System ◽  
Virtual Reality ◽  
Blood Vessel ◽  
Deformation Model ◽  
Virtual Reality Simulator ◽  
Model Based

scholarly journals User Behavior Identification and Personalized Recommendation Based on Web Data Mining

2021 ◽  
Vol 15 ◽  
pp. 643-650
Author(s):  
Ya Wang
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
User Behavior ◽  
Matrix Decomposition ◽  
Personalized Recommendation ◽  
K Nearest Neighbor ◽  
Web Data ◽  
Web Data Mining ◽  
User Behavior Analysis ◽  
Model Based

A good understanding of user behavior and consumption preferences can provide support for website operators to improve their service quality. However, the existing personalized recommendation systems generally have problems such as low Web data mining efficiency, low degree of automated recommendation, and low durability. Targeting at these unsolved issues, this paper mainly carries out the following works: Firstly, the authors established a user behavior identification and personalized recommendation model based on Web data mining, it gave the user behavior analysis process based on Web data mining, improved the traditional k-means algorithm, and gave the detailed execution steps of the improved algorithm; moreover, it also elaborated on the K nearest neighbor model based on user scoring information, the score matrix decomposition method, and the personalized recommendation method for network users. At last, experimental results verified the effectiveness of the constructed model.

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