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

针对现有词包模型对目标识别性能的不足,对特征提取、图像表示等方面进行改进以提高目标识别的准确率。首先,以密集提取关键点的方式取代SIFT关键点提取,减少了计算时间并最大程度地描述了图像底层信息。然后采用尺度不变特征变换(Scale-invariant feature transform,SIFT)描述符和统一模式的局部二值模式(Local binary pattern,LBP)描述符描述关键点周围的形状特征和纹理特征,引入K-Means聚类算法分别生成视觉词典,然后将局部描述符进行近似局部约束线性编码,并进行最大值特征汇聚。分别采用空间金字塔匹配生成具有空间信息的直方图,最后将金字塔直方图相串联,形成特征的图像级融合,并送入SVM进行分类识别。在公共数据库中进行实验,实验结果表明,本文所提方法能取得较高的目标识别准确率。

For the deficiency of the existing words bag in object recognition. We improve the teature ex- traction and image representation etc to enhance the accuracy. Firstly, a fixed step size is used and scale- intensive 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 histo- grams 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 pro- posed method can achieve higher recognition accuracy.