A novel localized and second order feature coding network for image recognition
By a News Reporter-Staff News Editor at Journal of Robotics & Machine Learning -- Data detailed on Pattern Analysis have been presented. According to news reporting originating in Guangdong, People’s Republic of China, by VerticalNews journalists, research stated, “Vector of Locally Aggregated Descriptor (VLAD) is a very popular feature coding method in image classification and image retrieval. Recently, the original VLAD method is extended to an end-to-end model called NetVLAD.”
Financial support for this research came from National Natural Science Foundation of China.
The news reporters obtained a quote from the research from the South China University of Technology, “The NetVLAD layer is readily embedded into a deep neural network and can be trained by the back-propagation algorithm. Although the NetVLAD model has achieved noticeable classification results in many image databases, the discrimination embedded in the NetVLAD method is not fully exploited. In this paper, in order to design a more discriminative feature coding network, a novel localized and second-order VLAD Network (LSO-VLADNet) is proposed. First, we design a localized and second-order VLAD coding method. Second, the back propagation functions of all newly designed layers are obtained. Third, the new feature coding method is extended to an end-to-end feature coding network which can be jointly trained with a deep convolutional neural network for visual recognition. Some experiments show that the newly designed network has the significant improvements over the original NetVLAD.”
According to the news reporters, the research concluded: “Some experimental comparisons of the proposed model and other state-of-art methods will also be given to validate the effectiveness of the proposed model.”
For more information on this research see: A novel localized and second order feature coding network for image recognition. Pattern Recognition , 2018;76():339-348. Pattern Recognition can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England. (Elsevier - www.elsevier.com; Pattern Recognition - http://www.journals.elsevier.com/pattern-recognition/)
Our news correspondents report that additional information may be obtained by contacting B.H. Chen, South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Guangdong, People’s Republic of China. Additional authors for this research include J. Li, G. Wei and B.Y. Ma.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.patcog.2017.10.039. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-03-12), Researchers from South China University of Technology Detail New Studies and Findings in the Area of Pattern Analysis (A novel localized and second order feature coding network for image recognition), Journal of Robotics & Machine Learning, 277, ISSN: 1944-186X, BUTTER® ID: 015304168
From the newsletter Journal of Robotics & Machine Learning.
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