沈阳工业大学学报2017，Vol.39Issue(5)：529-534,6.DOI:10.7688/j.issn.1000-1646.2017.05.10
基于Gabor滤波器和深度学习的图像检索方法
Image retrieval method based on Gabor filter and deep learning
摘要
Abstract
To solve the problem that the image database is becoming larger, an image retrieval method combined with both feature extraction and deep learning was investigated, and a model for feature extraction and dimensionality reduction was proposed based on Gabor wavelet transformation and restricted Boltzmann machine ( RBM) . The whole image was divided into local image blocks, and a set of Gabor filters were used to extract the image features, and the image features were studied and encoded with RBM. Hence, the dimensionality reduction of image features could be achieved. An image retrieval algorithm based on both deep belief networks ( DBN) and Softmax classifier was adopted. In addition, the Corel image database was used to perform the image retrieval test for the new method, and was compared with other two methods. The results show that the proposed method has better performance in precision rate, recall rate and retrieval time, and can obtain better image retrieval results.关键词
图像检索/Gabor小波/特征提取/降维/深度学习/受限玻尔兹曼机/深度信念网络/分类器Key words
image retrieval/Gabor wavelet/feature extraction/dimensionality reduction/deep learning/restricted Boltzmann machine( RBM)/deep belief network( DBN)/classifier分类
信息技术与安全科学引用本文复制引用
徐娟娟,陈晨,杨洪军..基于Gabor滤波器和深度学习的图像检索方法[J].沈阳工业大学学报,2017,39(5):529-534,6.基金项目
国家自然科学基金资助项目(61403160) （61403160）
内蒙古高等学校科学研究基金资助项目(NJZY6558). （NJZY6558）
