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- 저자 : Jiangfeng Ma (검색결과 4 건)
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Action Recognition Using Polyhedron Neighborhood Features
Jiangfeng Yang,Zheng Ma 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.1
To utilize the geometry structure information and similarity information within the neighborhood surrounding a spatio-temporal interest point for human action recognition task, we employ the axes of a regular polyhedron as a reference locating system, and build a novel local feature named polyhedron neighborhood feature (PNF). Then, to reduce quantization error in the coding stage, locality-constrained linear coding method is used to encode the obtained PNFs. Next, multi-temporal-scale PNFs (MPNFs) are created for handling the problem of various action speeds. In classification, support vector machine (SVM) based on linear kernel is used as classifier taking time consumption into account. The experiments on the KTH and UCF sports datasets show that the recognition system based on PNFs achieves better performance than the competing local spatio-temporal feature-based human action recognition methods.
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Action Recognition Based on Multi-scale Oriented Neighborhood Features
Jiangfeng Yang,Zheng Ma,Mei Xie 보안공학연구지원센터(IJSIP) 2015 International Journal of Signal Processing, Image Vol.8 No.1
The spatio-temporal (ST) position information between local features plays an important role in action recognition task. To use the information, neighborhood-based features are built for describing local ST information around ST interest points. However, traditional methods of constructing neighborhood, such as sub-ST volumetric method and nearest-neighbor-based neighborhood method, ignore the orientation information of neighborhood. To make the neighborhood-based features more discriminative, we construct a novel, oriented neighborhood by imposing weights on the distance components. Specifically, in our scheme, firstly, local features are produced, and encoded by locality-constrained linear coding (LLC). Then, oriented neighborhoods are constructed by imposing weights on the distance components between features, and obtain single-scale oriented neighborhood features (SONFs). Next, multi-scale oriented neighborhood features (MONFs) are formed by concatenating SONFs. As a result, action video sequences are represented as a collection of MONFs. Finally, locality-constrained group sparse representation (LGSR) is used as classifier upon MONFs. Experimental results on the KTH and UCF Sports datasets show that our method achieves better performance than the competing local ST feature-based human action recognition methods.
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Action Recognition Based on Spatio-temporal Log-Euclidean Covariance Matrix
Shilei Cheng,Jiangfeng Yang,Zheng Ma,Mei Xie 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.2
In this paper, we handle the problem of human action recognition by combining covariance matrices as local spatio-temporal (ST) descriptors and local ST features extracted densely from action video. Unlike traditional methods that separately utilizing gradient-based feature and optical flow-based feature, we use covariance matrix to fuse the two types of feature. Since covariance matrices are Symmetric Positive Definite (SPD) matrices, which form a special type of Riemannian manifold. To measure the distance of SPDs while avoid computing the geodesic distance between them, covariance features are transformed to log-Euclidean covariance matrices (LECM) by matrix logarithm operation. After encoding LECM by Locality-constrained Linear Coding method, in order to provide position information to ST-LECM features, spatial pyramid is used to partition the video frames, and the average-pooling-on-absolute-value function is implemented over each sub-frames. Finally, non-linear support vector machine is used as classifier. Experiments on public human action datasets show that the proposed method obtains great improvements in recognition accuracy, in comparison to several state-of-the-art methods.
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Xiaozhan Li,Wenming Zhang,Mingke Wu,Fengxue Xin,Weiliang Dong,Hao Wu,Min Zhang,Jiangfeng Ma,Min Jiang 한국생물공학회 2017 Biotechnology and Bioprocess Engineering Vol.22 No.5
Succinic acid is a platform chemical with potential for bio-based synthesis. However, the production of bio-based succinate is limited because of insufficient succinate efflux capacity in the late stage of fermentation. In the present study, three different transporters, which have been reported to be responsible for C4-dicarboxylates transport, were employed for investigation of the transport capacity of succinate in Escherichia coli. After engineered strains were constructed, the fermentative production of succinic acid was studied in serum bottles and 3 L of fermentor. The results demonstrated that engineered strain showed better efflux capacity than control strain under high concentration of succinate. The highest production of succinate was 68.66 g/L, while the NCgl2130 transporter may be the best candidate for succinate export in E. coli. Further research showed that the expression levels and relative enzyme activities involved in the metabolic pathway all increased markedly, and the maximum activities of PPC, PCK, PYK, and MDH increased by 1.50, 1.38, 1.28, and 1.27-fold in recombinant E. coli AFP111/pTrc99a- NCgl2130, respectively. Moreover, the maximum level of intracellular ATP increased by 23.79% in E. coli AFP111/ pTrc99a-NCgl2130. Taken together, these findings indicated that engineered transporters can improve succinate production by increasing key enzyme activities and intracellular ATP levels. To the best of thew authors’ knowledge, this is the first report on a mechanism to improve succinate production by engineered transporters. This strategy set up a foundation for improving the biosynthesis of other C4-dicarboxylates, such as fumaric acid and malic acid.
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