Image retrieval method based on Gabor filter and deep learning
XU Juan-juan
CHEN Chen
YANG Hong-jun
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.
Keywords：image retrievalGabor waveletfeature extractiondimensionality reductiondeep learningrestricted Boltzmann machine( RBM)deep belief network( DBN)classifier
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 529-534 )