Image retrieval method based on CNN and dimension reduction

An image retrieval method based on convolution neural network and dimension reduction is proposed in this paper. Convolution neural network is used to extract high-level features of images, and to solve the problem that the extracted feature dimensions are too high and have strong correlation, multilinear principal component analysis is used to reduce the dimension of features. The features after dimension reduction are binary hash coded for fast image retrieval. Experiments show that the method proposed in this paper has better retrieval effect than the retrieval method based on principal component analysis on the e-commerce image datasets.

[1]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[2]  Jen-Hao Hsiao,et al.  Deep learning of binary hash codes for fast image retrieval , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[3]  Hongxun Yao,et al.  Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models , 2017, International Conference on Digital Image Processing.

[4]  Jinyu Li,et al.  Feature Learning in Deep Neural Networks - Studies on Speech Recognition Tasks. , 2013, ICLR 2013.

[5]  Haiping Lu,et al.  MPCA: Multilinear Principal Component Analysis of Tensor Objects , 2008, IEEE Transactions on Neural Networks.

[6]  Aren Jansen,et al.  CNN architectures for large-scale audio classification , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Victor S. Lempitsky,et al.  Neural Codes for Image Retrieval , 2014, ECCV.

[8]  Yu Zhang,et al.  Advances in Joint CTC-Attention Based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM , 2017, INTERSPEECH.

[9]  I. Jolliffe Principal Component Analysis , 2002 .

[10]  Demetri Terzopoulos,et al.  Multilinear Analysis of Image Ensembles: TensorFaces , 2002, ECCV.

[11]  Naphtali Rishe,et al.  Content-based image retrieval , 1995, Multimedia Tools and Applications.

[12]  Ping Wang,et al.  Content-based image retrieval based on CNN and SVM , 2016, 2016 2nd IEEE International Conference on Computer and Communications (ICCC).

[13]  Piotr Indyk,et al.  Approximate nearest neighbors: towards removing the curse of dimensionality , 1998, STOC '98.

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

[15]  Hanjiang Lai,et al.  Supervised Hashing for Image Retrieval via Image Representation Learning , 2014, AAAI.

[16]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[17]  Xiaojun Wu,et al.  分块多线性主成分分析及其在人脸识别中的应用研究 (Modular Multilinear Principal Component Analysis and Application in Face Recognition) , 2015, 计算机科学.

[18]  Ye Kyaw Thu,et al.  Exploring the Effect of Tones for Myanmar Language Speech Recognition Using Convolutional Neural Network (CNN) , 2017, PACLING.

[19]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.