数据采集与处理2017，Vol.32Issue(3)：489-496,8.DOI:10.16337/j.1004-9037.2017.03.007
基于词包和特征融合的目标识别算法
Object Recognition Algorithm Based on Bag of Words and Feature Fusion
摘要
Abstract
For the deficiency of the existing words bag in object recognition.We improve the feature extraction and image representation etc to enhance the accuracy.Firstly,a fixed step size is used and scaleintensive is fixed to extract key points,and then the scale-invariant feature transform (SIFT) and local binry pattern(LBP) around the key points in the grids are extracted to describe the shape features and texture features.K-Means clustering algorithm is introduced to generate a visual dictionary and the local descriptors are encoded by approximated locality constrained linear coding,and max pooling and a histograms are generated using spatial pyramid matching.Both the spatial pyramid histograms are connected,therefore,the feature fusion in the image level is implemented under the words bag.Finally the fusion result is sent to the SVM for classification.Experimental result in public datasets shows that the proposed method can achieve higher recognition accuracy.关键词
词包模型/目标识别/形状特征/纹理特征Key words
bag of words/object recognition/shape features/texture features分类
信息技术与安全科学引用本文复制引用
周治平,李文慧,周明珠..基于词包和特征融合的目标识别算法[J].数据采集与处理,2017,32(3):489-496,8.基金项目
江苏省自然科学基金(BK20131107)资助项目. （BK20131107）
