| 基于证据理论融合多特征的物体识别算法 |
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| 引用本文： | 孙晋博,余隋怀,陈登凯.基于证据理论融合多特征的物体识别算法[J].计算机工程与应用,2015,51(9):147-151. |
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| 作者姓名： | 孙晋博 余隋怀 陈登凯 |
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| 作者单位： | 西北工业大学 机电学院 工业设计研究所，西安 710072 |
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| 基金项目： | 国家高技术研究发展计划(863)(No.2009AA093303)；高等学校学科创新引智计划(No.B13044)。 |
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| 摘 要： | 为了提高物体的识别正确率，提出一种基于证据理论融合多特征的物体识别算法。提取物体图像的颜色直方图和尺度不变特征，采用极限学习机建立相应的图像分类器，根据单一特征的识别结果构建概率分配函数，并采用证据理论对单一特征识别结果进行融合，得出物体的最终识别结果，采用多个图像数据库对算法有效性进行测试。测试结果表明，该算法不仅提高了物体的识别率，而且加快了物体识别的速度，具有一定的实际应用价值。
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| 关 键 词： | 物体识别 证据理论 极限学习机 尺度不变特征变换 颜色直方图 |
Object recognition method based on multi-feature fusion of D-S evidence theory |
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| SUN Jinbo,YU Suihuai,CHEN Dengkai.Object recognition method based on multi-feature fusion of D-S evidence theory[J].Computer Engineering and Applications,2015,51(9):147-151. |
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| Authors: | SUN Jinbo YU Suihuai CHEN Dengkai |
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| Affiliation: | Institute of Industrial Design, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China |
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| Abstract: | In order to obtain better recognition results, a novel object recognition method based on multi-feature fusion of evidence theory is proposed. Color histogram and scale invariant feature transform features are extracted from object image, and extreme learning machine is used to establish the classifier; the recognition results of single feature are fused to obtain the last recognition results of object based on evidence theory; the performance of algorithm is tested by some image data. The result illustrates that the proposed algorithm has improved the recognition rate and speed, and it has some application vale. |
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| Keywords: | object recognition evidence theory extreme learning machine scale invariant feature transform color histogram |
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