中文摘要：

结合图像属性上下文信息和核熵成分分析,构造了一种新颖的基于下上文信息的局部特征描述子——上下文核描述子（Context Kernel Descriptors,CKD）.上下文信息的引入提高了CKD特征的鲁棒性,减少了特征误匹配.核熵成分分析从全维CKD特征分量中选出最能代表目标几何结构信息的特征分量,将其投影到这些特征分量张成的子空间上可得到降维CKD特征.在Caltech-101和CIFAR-10的测试结果表明,CKD的分类性能不仅明显优于其它局部特征描述子,还优于多数基于稀疏表示和深度学习等复杂模型的目标分类算法.

英文摘要：

Combining the context cue of image attributes and kernel entropy component analysis（ KECA）,w e proposed a context-based local feature descriptor called context kernel descriptors（ CKD）. Context cue implied in the CKD improves its robustness,thus reducing false matches during feature correspondence. KECA applied in the feature dimensionality reduction step selects the principal eigenvectors that contribute most to the geometrical structure of input images. Projecting the full-dimensional CKD onto the subspaces spanned by these principal eigenvectors,w e derive the final low-dimensional CKD. Evaluation results on Caltech-101 and CIFAR-10 show that the classification performance of the proposed CKD significantly outperforms other local descriptors,and even surpasses most sparse representation-based and deep learning-based sophisticated object classification methods.