Deep-based Self-refined Face-top Coordination

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Honglin Li ◽  
Xiaoyang Mao ◽  
Mengdi Xu ◽  
Xiaogang Jin
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
User Study ◽  
Experimental Results ◽  
Individual Preferences ◽  
Optimum Path ◽  
General Preferences

Face-top coordination, which exists in most clothes-fitting scenarios, is challenging due to varieties of attributes, implicit correlations, and tradeoffs between general preferences and individual preferences. We present a Deep-Based Self-Refined (DBSR) system to simulate face-top coordination based on intuition evaluation. To this end, we first establish a well-coordinated face-top (WCFT) dataset from fashion databases and communities. Then, we use a jointly trained CNN Deep Canonical Correlation Analysis (DCCA) method to bridge the semantic face-top gap based on the WCFT dataset to deal with general preferences. Subsequently, an irrelevance-based Optimum-path Forest (OPF) method is developed to adapt the results to individual preferences iteratively. Experimental results and user study demonstrate the effectiveness of our method.

ROBUST CO-TRAINING

2011 ◽  
Vol 25 (07) ◽  
pp. 1113-1126 ◽  
Author(s):  
SHILIANG SUN ◽  
FENG JIN
Keyword(s):  
Correlation Analysis ◽  
Supervised Learning ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Learning Algorithm ◽  
Experimental Results ◽  
The Other ◽  
Classification Problems ◽  
Training Examples ◽  
Low Dimensional

Co-training is a multiview semi-supervised learning algorithm to learn from both labeled and unlabeled data, which iteratively adopts a classifier trained on one view to teach the other view using some confident predictions given on unlabeled examples. However, as it does not examine the reliability of the labels provided by classifiers on either view, co-training might be problematic. Even very few inaccurately labeled examples can deteriorate the performance of learned classifiers to a large extent. In this paper, a new method named robust co-training is proposed, which integrates canonical correlation analysis (CCA) to inspect the predictions of co-training on those unlabeled training examples. CCA is applied to obtain a low-dimensional and closely correlated representation of the original multiview data. Based on this representation the similarities between an unlabeled example and the original labeled examples are determined. Only those examples whose predicted labels are consistent with the outcome of CCA examination are eligible to augment the original labeled data. The performance of robust co-training is evaluated on several different classification problems where encouraging experimental results are observed.

Analysis of Maximum Expiratory Flow Volume Curves Using Canonical Correlation Analysis

1985 ◽  
Vol 24 (02) ◽  
pp. 91-100 ◽  
Author(s):  
W. van Pelt ◽  
Ph. H. Quanjer ◽  
M. E. Wise ◽  
E. van der Burg ◽  
R. van der Lende
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Flow Volume ◽  
Non Linear ◽  
Maximum Expiratory Flow ◽  
Expiratory Flow ◽  
Relationship Of ◽  
The Relationship ◽  
Age And Sex

SummaryAs part of a population study on chronic lung disease in the Netherlands, an investigation is made of the relationship of both age and sex with indices describing the maximum expiratory flow-volume (MEFV) curve. To determine the relationship, non-linear canonical correlation was used as realized in the computer program CANALS, a combination of ordinary canonical correlation analysis (CCA) and non-linear transformations of the variables. This method enhances the generality of the relationship to be found and has the advantage of showing the relative importance of categories or ranges within a variable with respect to that relationship. The above is exemplified by describing the relationship of age and sex with variables concerning respiratory symptoms and smoking habits. The analysis of age and sex with MEFV curve indices shows that non-linear canonical correlation analysis is an efficient tool in analysing size and shape of the MEFV curve and can be used to derive parameters concerning the whole curve.

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