Jinye Peng
Northwest University (China)
14 Papers
54 Citations
Jinye Peng is an academic researcher from Northwest University (China). The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 8, co-authored 14 publications. Previous affiliations of Jinye Peng include Northwestern Polytechnical University.
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Papers
Learning inter-related visual dictionary for object recognition
Ning Zhou,Yi Shen,Jinye Peng,Jianping Fan +3 more
- 16 Jun 2012
TL;DR: A novel joint dictionary learning (JDL) algorithm to exploit the visual correlation within a group of visually similar object categories for dictionary learning where a commonly shared dictionary and multiple category-specific dictionaries are accordingly modeled.
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Image collection summarization via dictionary learning for sparse representation
TL;DR: The proposed dictionary learning approach for sparse representation model to construct the summary and to represent the image can obtain more accurate results as compared with other six baseline summarization algorithms.
87
Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification
TL;DR: The experimental results have demonstrated the effectiveness of the hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers over the visual tree for large-scale plant species identification.
78
Image collection summarization via dictionary learning for sparse representation
Chunlei Yang,Jinye Peng,Jianping Fan +2 more
- 16 Jun 2012
TL;DR: A novel framework is developed to achieve effective summarization of large-scale image collection by treating the problem of automatic image summarization as a problem of dictionary learning for sparse representation, and the results have shown that the dictionarylearning for sparse representations algorithm can obtain more accurate summary as compared with other baseline algorithms.
29
Hierarchical Classification of Large-Scale Patient Records for Automatic Treatment Stratification
TL;DR: A hierarchical learning algorithm is developed for classifying large-scale patient records into large numbers of known patient categories for automatic treatment stratification and can achieve log-linear computational complexity, which is very attractive for big data applications.
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