Image Retrieval Using Fused Deep Convolutional Features

This paper proposes an image retrieval using fused deep convolutional features to solve the semantic gap between low-level features and high-level semantic features of traditional contend-based image retrieval method. Firstly, the improved network architecture LeNet-L is obtained by improving convolutional neural network LeNet-5. Then, fusing two different deep convolutional features which are extracted by LeNet-5 and AlexNet. Finally, after the fusion, the similar image is obtained through comparing the similarity between the image being retrieved and the image in database by distance function. In Corel dataset, this method is compared with the single convolutional neural network extracted features for image retrieval method, it has a higher precision and recall. The results show that this method has a better retrieval accuracy.

[1]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[2]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Qi Tian,et al.  Fine-Grained Image Search , 2015, IEEE Transactions on Multimedia.

[4]  Shuo Wang,et al.  Overview of deep learning , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[5]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[6]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[7]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[9]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.