Abstract Stacked denoising auto-encoder(SDA) is introduced into the image recognition. Convolutional auto-encoder (CAE) is used to improve SDA in the area of natural image retrieval. The unsupervised greedy layer-wise training algorithm is used to initialize the weight of the network. The parameters of the network are optimized by the back propagation algorithm. The improved SDA is trained for extracting features from natural images and the softmax classifier is used for classification. Finally, the extracted feature combined with scale invariant feature transform (SIFT) is used for realizing images retrieval. The experimental results show that the improved stacked denoising auto-encoder(ISDA) method can greatly reduce the time of network training, enhance the fault-tolerant ability of network, raise the classification precision of the classifier and eventually improve the image retrieval performance.
2014, Vol. 27 
