Richer feature for image classification with super and sub kernels based on deep convolutional neural network

The concepts of super convolutional kernel and sub convolutional kernel are proposed inspired by the process that human observes objects.A novel Parallel Crossing Deep Convolutional Neural Network (PCDCNN) model is designed to simulate the human binocular mechanism for extracting richer information.A framework named multi-scale PCDCNN is deployed based on super convolutional kernel and sub convolutional kernel for image classification. Display Omitted Deep convolutional neural network (DCNN) has obtained great successes for image classification. However, the principle of human visual system (HVS) is not fully investigated and incorporated into the current popular DCNN models. In this work, a novel DCNN model named parallel crossing DCNN (PCDCNN) is designed to simulate HVS with the concepts of super convolutional kernel and sub convolutional kernel being introduced. Moreover, a multi-scale PCDCNN (MS-PC-DCNN) framework is designed, with which a batch of PCDCNN models are deployed and the scores from each PCDCNN model are fused by weighted average for the final prediction. The experimental results on four public datasets verify the superiority of the proposed model as compared to a number of state-of-the-art models.

[1]  Yoshua Bengio,et al.  Maxout Networks , 2013, ICML.

[2]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  M. Usman Akram,et al.  Person identification using vascular and non-vascular retinal features , 2016, Comput. Electr. Eng..

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

[5]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[6]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[7]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[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.

[10]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[11]  Zhuowen Tu,et al.  Deeply-Supervised Nets , 2014, AISTATS.

[12]  Qingming Huang,et al.  Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition , 2014, Comput. Vis. Image Underst..

[13]  Fei-Fei Li,et al.  Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going? , 2013, 2013 IEEE International Conference on Computer Vision.

[14]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[15]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[16]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Thomas Brox,et al.  Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[19]  Rob Fergus,et al.  Stochastic Pooling for Regularization of Deep Convolutional Neural Networks , 2013, ICLR.

[20]  Yann LeCun,et al.  Regularization of Neural Networks using DropConnect , 2013, ICML.

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

[22]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[23]  Jian Sun,et al.  Convolutional neural networks at constrained time cost , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Thomas Brox,et al.  Discriminative Unsupervised Feature Learning with Convolutional Neural Networks , 2014, NIPS.