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。
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.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第9期147-151,共5页
Computer Engineering and Applications
基金
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)
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
evidence theory
extreme learning machine
scale invariant feature transform
color histogram
