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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
A Method for Improving SVM-based Image Classification Performance Based on a Target Object Detection Scheme
| Title: | A Method for Improving SVM-based Image Classification Performance Based on a Target Object Detection Scheme |
| Authors: | Yoshida, Soh Browse this author | | Okada, Hiroshi Browse this author | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | image classification | | object detection | | support vector machine |
| Issue Date: | 1-Jul-2013 |
| Publisher: | The Institute of Image Information and Television Engineers |
| Journal Title: | ITE Transactions on Media Technology and Applications |
| Volume: | 1 |
| Issue: | 3 |
| Start Page: | 237 |
| End Page: | 243 |
| Publisher DOI: | 10.3169/mta.1.237 |
| 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. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/53271 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 長谷山 美紀
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