Title: | Integration of Reconstruction Error Obtained by Local and Global Kernel PCA with Different Role |
Authors: | Hotta, Kazuhiro |
Citation: | WSCG '2011: Communication Papers Proceedings: The 19th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 91-98. |
Issue Date: | 2011 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | http://wscg.zcu.cz/WSCG2011/!_2011_WSCG-Short_Papers.pdf http://hdl.handle.net/11025/10826 |
ISBN: | 978-80-86943-82-4 |
Keywords: | klasifikace scény;kernelová analýza hlavních komponent |
Keywords in different language: | kernel principal component analysis;scene classification |
Abstract: | This paper presents a scene classification method using the integration of the reconstruction errors by local Kernel Principal Component Analysis (KPCA) and global KPCA. There are some methods for integrating local and global features. However, it is important to give obvious different role to each feature. In the proposed method, global feature with topological information represents the rough composition of scenes and local feature without position information represents fine part of scenes. Experimental results show that accuracy is improved by using the reconstruction errors obtained from the different point of views. The proposed method is much better than only local KPCA, global KPCA and linear Support Vector Machine (SVM) of bag-of-visual words with the same basic feature. Our method is also comparable to conventional methods using the same database. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG '2011: Communication Papers Proceedings |
Please use this identifier to cite or link to this item:
http://hdl.handle.net/11025/10826
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.