关键词: 非监督学习, 特征描述, 卷积自编码器, 信息熵

Abstract: To solve the problem that low-level features extracted by unsupervised learning methods are easily disturbed by image’s rotation and scaling as well as difficult to distinguish when used in feature description, a local feature description algorithm is proposed based on improved Convolutional Auto-Encode（CAE-D）. Evaluating the convolution kernel’s performance by information entropy, a convolution kernel’s entropy constraint rule is proposed to improve the distinguish ability of convolution feature description through convolution kernels carrying local information. Traditional SIFT’s orientation assignment algorithm is used to assign the main direction of local image before feature description, and the feature-map is down-sampled to enhance rotation-invariance and robustness of the feature description. The results of image matching show that CAE-D is competitive with the performance of KAZE and SIFT descriptor in geometric and photometric deformations and takes 47. 14% less time than SIFT.

Key words: unsupervised learning, feature description, convolutional auto-encoder, information entropy