Abstract: A similar image retrieval method based on nested network model is proposed by improving the traditional dense network (DenseNet) which is a common deep-learning method. First, the proposed algorithm optimizes feature retrieving blocks by embedding squeeze-and-excitation network (SENet) into the original DenseNet and adjusting the structure of the DenseNet, so as to improve the accuracy of image retrieval. Second, the algorithm achieves the speed-up of image retrieval by setting proper weight for each feature channel of the image, thus suppressing the processing time of those invalid features. Experimental results show that the algorithm can strengthen the transmission of effective image features and improve the accuracy of image researching results effectively.

Key words: dense convolutional network (DenseNet), squeezing-and-excitation network(SENet), nesting, suppress invalid feature, image retrieval