基于LLC与GIST特征的静态人体行为分类

Static Human Behavior Classification Based on LLC and GIST Features

针对静态图像人体行为识别问题,提出一种融合局部约束线性编码(LLC)和全局特征描述子的方法。该方法对图像进行密集采样,提取每个子区域的SIFT特征,利用LLC方法对提取的密集SIFT特征进行编码和池化。为了加入空间信息,采用空间金字塔的思想,获得具有空间位置信息的LLC池化特征。将LLC池化特征串联通用搜索树(GIST)特征作为图像的最终描述,使用核函数为直方图交叉核函数的支持向量机进行分类。实验结果表明,与利用LLC、空间金字塔匹配特征和GIST特征进行识别的方法相比,该方法识别效果较好。

Aiming at the problem of static image human behavior recognition,a method of merging Local-constrained Linear Coding（ LLC） and global feature descriptors is proposed. This method makes intensive sampling of the image,and extracts the Scale-Invariant Feature Transform（ SIFT） features in each subregion. Then the LLC method is used to encode and pool the dense SIFT features. In order to add spatial information,spatial pyramid information is used to extract LLC pooling features with spatial location information. Generalized Search Trees（ GIST） features are extracted,and the final features of the image are described using LLC pooling features tandem GIST features. The kernel function is the histogram cross kernel function Support Vector M achine（ SVM） and it is used for classification. Experimental results show that compared with the methods using LLC,Spatial Pyramid M atching（ SPM） features and GIST features,the proposed method has better recognition effect.