Feature Fusion in Part-Based Object Detection

被引：0
|
作者：
Koyuncu, Murat [1 ]
Cetinkaya, Basar [2 ]
机构：
[1] Atilim Univ, Bilisim Sistemleri Muhendisligi Bolumu, Ankara, Turkey
[2] Atilim Univ, Muhendisl Sistemlerinin Tasarimi & Modellenmesi, Ankara, Turkey
来源：
2015 23RD SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2015年
关键词：
Component based object detection; SVM; feature vectors; fusion; RETRIEVAL;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this study, classification of complex objects in images as a whole is compared with classification of its distinctive components using different features. In addition, the impact of feature fusion in part-based object detection is investigated. Applied method, implemented system, conducted tests and their results are presented in this paper. Test results show that, even in the case of a good segmentation, object components are classified more successfully compared to whole object and feature fusion method improves the obtained results to a certain degree.
引用
收藏
页码：565 / 568
页数：4
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