[Paper] A Method for Improving SVM-based Image Classification Performance Based on a Target Object Detection Scheme
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- Yoshida Soh
- Graduate School of Information Science and Technology, Hokkaido University
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- Okada Hiroshi
- Graduate School of Information Science and Technology, Hokkaido University
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- Ogawa Takahiro
- Graduate School of Information Science and Technology, Hokkaido University
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- Haseyama Miki
- Graduate School of Information Science and Technology, Hokkaido University
Abstract
This paper presents a new method to improve performance of SVM-based classification, which contains a target object detection scheme. The proposed method tries to detect target objects from training images and improve the performance of the image classification by calculating the hyperplane from the detection results. Specifically, the proposed method calculates a Support Vector Machine (SVM) hyperplane, and detects rectangular areas surrounding the target objects based on the distances between their feature vectors and the separating hyperplane in the feature space. Then modification of feature vectors becomes feasible by removing features that exist only in background areas. Furthermore, a new hyperplane is calculated by using the modified feature vectors. Since the removed features are not part of the target object, they are not relevant to the learning process. Therefore, their removal can improve the performance of the image classification. Experimental results obtained by applying the proposed methods to several existing SVM-based classification method show its effectiveness.
Journal
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- ITE Transactions on Media Technology and Applications
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ITE Transactions on Media Technology and Applications 1 (3), 237-243, 2013
The Institute of Image Information and Television Engineers
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Details 詳細情報について
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- CRID
- 1390001205423975936
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- NII Article ID
- 130004648168
- 120005326737
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- ISSN
- 21867364
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- HANDLE
- 2115/53271
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- Text Lang
- en
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- Data Source
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- JaLC
- IRDB
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed