Image Classification Algorithm Based on Deep Learning-Kernel Function

Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy, and weak adaptive ability. This method separates image feature extraction and classification into two steps for classification operation. The deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. However, this method has the following problems in the application process: first, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. And a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  Vijayan K. Asari,et al.  The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches , 2018, ArXiv.

[3]  Yoshua Bengio,et al.  Classification using discriminative restricted Boltzmann machines , 2008, ICML '08.

[4]  Paul Tu,et al.  Multi-image mosaic with SIFT and vision measurement for microscale structures processed by femtosecond laser , 2018 .

[5]  Andrew Zisserman,et al.  Convolutional Two-Stream Network Fusion for Video Action Recognition , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[8]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Xiaogang Wang,et al.  T-CNN: Tubelets With Convolutional Neural Networks for Object Detection From Videos , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Sam Kwong,et al.  G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition , 2017, Neurocomputing.

[11]  Alexander Ferworn,et al.  Low-cost 3D scene reconstruction for response robots in real-time , 2011, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics.

[12]  Bingbing Ni,et al.  HCP: A Flexible CNN Framework for Multi-Label Image Classification , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[14]  Honglak Lee,et al.  An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.

[15]  Thomas J. Hebert,et al.  Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm , 1998, IEEE Trans. Image Process..

[16]  Hongwei Liu,et al.  Convolutional Neural Network With Data Augmentation for SAR Target Recognition , 2016, IEEE Geoscience and Remote Sensing Letters.

[17]  Roberto de Alencar Lotufo,et al.  Fingerprint Liveness Detection Using Convolutional Neural Networks , 2016, IEEE Transactions on Information Forensics and Security.

[18]  Fabio Poiesi,et al.  Online Multi-target Tracking with Strong and Weak Detections , 2016, ECCV Workshops.

[19]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[20]  Tommy W. S. Chow,et al.  Sparse Codes Auto-Extractor for Classification: A Joint Embedding and Dictionary Learning Framework for Representation , 2016, IEEE Transactions on Signal Processing.

[21]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[22]  Bohyung Han,et al.  Learning Multi-domain Convolutional Neural Networks for Visual Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Xiaogang Wang,et al.  STCT: Sequentially Training Convolutional Networks for Visual Tracking , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Li Zhang,et al.  Structured Latent Label Consistent Dictionary Learning for Salient Machine Faults Representation-Based Robust Classification , 2017, IEEE Transactions on Industrial Informatics.

[25]  Xiaoou Tang,et al.  A large-scale car dataset for fine-grained categorization and verification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  Wang Mei,et al.  SAR Image Target Recognition Based on Hu Invariant Moments and SVM , 2009, 2009 Fifth International Conference on Information Assurance and Security.

[27]  Q. M. Wu,et al.  Fingerprint Liveness Detection from Different Fingerprint Materials Using Convolutional Neural Network and Principal Component Analysis , 2018 .

[28]  Feng-Ping An,et al.  Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural Network , 2019, Complex..

[29]  Richa Singh,et al.  Class sparsity signature based Restricted Boltzmann Machine , 2017, Pattern Recognit..

[30]  Yuxin Peng,et al.  The application of two-level attention models in deep convolutional neural network for fine-grained image classification , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Luiz Eduardo Soares de Oliveira,et al.  A Dataset for Breast Cancer Histopathological Image Classification , 2016, IEEE Transactions on Biomedical Engineering.

[32]  Shiv Ram Dubey,et al.  Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases , 2015, IEEE Transactions on Image Processing.

[33]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  A. J. Connolly,et al.  REDUCING THE DIMENSIONALITY OF DATA: LOCALLY LINEAR EMBEDDING OF SLOAN GALAXY SPECTRA , 2009, 0907.2238.

[35]  Cordelia Schmid,et al.  P-CNN: Pose-Based CNN Features for Action Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[36]  Qi Tian,et al.  Image classification by non-negative sparse coding, low-rank and sparse decomposition , 2011, CVPR 2011.

[37]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[38]  Xiaogang Wang,et al.  Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Liang Xiao,et al.  Supervised Spectral–Spatial Hyperspectral Image Classification With Weighted Markov Random Fields , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[41]  Dacheng Tao,et al.  Robust Face Recognition via Multimodal Deep Face Representation , 2015, IEEE Transactions on Multimedia.

[42]  Junge Zhang,et al.  CNet: Context-Aware Network for Semantic Segmentation , 2017, 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR).

[43]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[44]  Fei-Fei Li,et al.  Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

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

[47]  Yurii Nesterov,et al.  Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems , 2012, SIAM J. Optim..

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

[49]  Xin Pan,et al.  A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

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

[51]  David Zhang,et al.  Multi-Label Dictionary Learning for Image Annotation , 2016, IEEE Transactions on Image Processing.

[52]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[53]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[54]  Heesung Kwon,et al.  Going Deeper With Contextual CNN for Hyperspectral Image Classification , 2016, IEEE Transactions on Image Processing.

[55]  Bei Zhao,et al.  Scene classification based on a hierarchical convolutional sparse auto-encoder for high spatial resolution imagery , 2017 .

[56]  Javier Ruiz-del-Solar,et al.  Object recognition using local invariant features for robotic applications: A survey , 2016, Pattern Recognit..

[57]  Gabriele Moser,et al.  Combining Support Vector Machines and Markov Random Fields in an Integrated Framework for Contextual Image Classification , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[58]  John G. Csernansky,et al.  Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.