Image classification by Distortion-Free Graph Embedding and KNN-Random forest

Image classification algorithms play an important role in various computer vision problems such as object tracking, image labeling, and object segmentation. A number of methodologies have been proposed to tackle this problem. One of the possible approaches employed extensively in the literature is to represent an image as a graph based on its handcrafted features. However, recent advancements in deep neural networks have shown their ability to learn more discriminative and representative features. Therefore, the deep features have become considerable alternatives of hand-crafted ones. In this paper, we propose a novel framework based on distortion-free graph embedding using deep features and KNN-Random forest. Our method outperforms the state-of-the-art graph embedding-based image classification approach for the task of image classification. Particularly, the proposed framework obtains 97.5% top - 1 image classification accuracy for the ImageNet dataset for 5 classes and 93.3% for 10 classes.

[1]  Forrest N. Iandola,et al.  SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.

[2]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[3]  Jun Li,et al.  Simultaneous Sparse Graph Embedding for Hyperspectral Image Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[4]  M. Fatih Demirci,et al.  Object Recognition as Many-to-Many Feature Matching , 2006, International Journal of Computer Vision.

[5]  Brijesh Verma,et al.  Impact of Automatic Feature Extraction in Deep Learning Architecture , 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[6]  Quoc V. Le,et al.  Self-Training With Noisy Student Improves ImageNet Classification , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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

[8]  Lei Shi,et al.  Supervised Graph Embedding for Polarimetric SAR Image Classification , 2013, IEEE Geoscience and Remote Sensing Letters.

[9]  Wenwu Zhu,et al.  Structural Deep Network Embedding , 2016, KDD.

[10]  Palash Goyal,et al.  Graph Embedding Techniques, Applications, and Performance: A Survey , 2017, Knowl. Based Syst..

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

[12]  Feiping Nie,et al.  Cauchy Graph Embedding , 2011, ICML.

[13]  Leonidas J. Guibas,et al.  The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.

[14]  Rama Chellappa,et al.  Unconstrained face verification using deep CNN features , 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

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

[16]  M. Fatih Demirci,et al.  Object Recognition by Distortion-Free Graph Embedding and Random Forest , 2016, 2016 IEEE Tenth International Conference on Semantic Computing (ICSC).

[17]  Jitendra Malik,et al.  SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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

[19]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[20]  Nikos Komodakis,et al.  Building detection in very high resolution multispectral data with deep learning features , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[21]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[23]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[25]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[26]  M. Fatih Demirci,et al.  Efficient many-to-many feature matching under the l1 norm , 2011, Comput. Vis. Image Underst..

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